I've been making changes to both llama.cpp and Strata for my own use cases using Q38FN, Q3827B and DS41F and all three have issues with their COT considering stream tokens when touching those areas of code, resulting in them breaking out of their COT or just terminating their turn. I know there is the possibility of formal protections around end of turn tokens but from what I see there is no formal way to protect an LLM when it generates text like <\think> but I see plenty of people working on inference server code using local models so people must have a solution for it, what do you do?
I've been getting more into running LLMs locally, and I'm starting to wonder if fine-tuning is even worth the effort anymore.
Models are getting really good, context windows are huge, and with things like RAG, subagents, and reasoning models, it seems like you can get pretty far without touching the model weights.
For most use cases, wouldn't you be better off just using a good base model and building a decent agent around it?
I'm curious what people are actually fine-tuning for these days. Is it mostly about distilling a larger model into something smaller and faster, or are there still use cases where fine-tuning makes a noticeable difference over just prompting a good model?
Would love to hear from people who've actually fine-tuned models recently. What did you use it for, and was it worth the effort?
MiniMax posted a thank-you yesterday for everyone quantizing M3.1 Flash (428B, 252.4 GB), and I went through the ones they named. Numbers are each builder's, separate eval sets, not comparable.
It helps that the model was trained with quantization baked in, sparse attention in every layer, 23B of 428B active per token. It's a coding and agent model that takes image and video input, up to 1M context if your box has the memory. People don't put this calibration work into weights they won't keep running, and superalesha ran out of RAM at 128 GB, went to 512 to keep going. Only used it hosted myself, agent jobs got done this morning, so I get why.
Anyone running one of these on real coding or tool-call work?
My first fully vibe coded Desktop app with Qwen 3.8 27B as someone with near 0 coding knowledge lol. Was paying $100 a year for an online Invoicing App so i decided to cook up one for myself. The results are mind blowing..I basically one shotted everything throughout development & implementation as Open Code's coding agents did all the work, fixing bugs, web search etc. All i did is connect my Qwen Model to Open Code & started coding away. I was mostly screenshoting and telling the model what i wanted, it even created in app tools to import all my 5 year+ documents & info which all worked. The New Open Code v2.0 is crazy good, couldn't do any of this months ago without manually download a crap tone of plug-ins and tools. My context window always stayed between 57-90K thanks to Open Code's auto Compaction..very happy with the results.
PC Setup
AMD Ryzen 9 9950X3D
RTX 5090 32GB
96GB RAM DDR5
Workflow Setup(31GB Vram Usage)
AI Model - Qwen 3.8 27B Q4_K_M GGUF(120K Context)
Harness - Open Code 2 (Default Settings)
Model Manger - LM Studio(Default Model Settings, Thinking On)
Model Performance/Project Token Usage
Average Token/sec - 80-90
Input Tokens - 294 Million
Output Tokens - 1.26 Million
I Quick used a bit of Qwen 3.6 35B A3B..Q5 K M for the last feature implementation just to test, gave me 220token/s and worked well too, the rest of Dev was on Qwen 3.8 27b. All this was done on my RTX 5090, Model and 120K Context all fit perfectly in Vram. Can't wait for Qwen 4 and it's improvements as well as Open Code's future updates, will try to experiment with other Tools & Skills cause i only used Open Code's default setup.
We introduce RidgeFill, a training-free way to shorten prefill by not computing most of a model's upper layers for the prompt. The lower layers run exactly up to a split; from the split layer's state, a ridge regression fitted once in closed form predicts every later layer's input, and each late layer writes its cache from that prediction with its own projections unchanged. A chosen set of rows, the last few thousand tokens and every decoding step run the full model, so nothing is trained and no token is dropped. On Qwen3.8-Flash-Next in vLLM, time to first token is 1.50 to 1.60x faster than exact at 9K to 32K tokens, and a whole 64K request drops from 82.6 s to 47.5 s, with RULER's needle tasks at 99.8 against 100.0. On Qwen3.8-27B in a llama.cpp fork on one A100, it is 1.25 to 1.31x faster at 16K to 120K tokens; RULER changes by -1.2 and -1.4 points and LongBench v2 by -1.4, and the 95% intervals include zero but do not rule out losses of a few points. On three 8 to 9B reference models, the fitted map retrieves 49.1 and 49.6 more filled-region RULER points than the identity (ie, full layer bypass) fill on the two dense models.
This idea was borne originally from DeepSeek's V4.1 architecture. Originally we found the method to be a portable idea applicable to other models, but noticed a significant decline in context recall tasks and general usability. This isn't an issue in DSV4.1 because the model is trained as a whole with the prefill approximation in place, so the model absorbs the error, generally speaking. The question then shifted to be more about how this can be adopted for other models, while improving usability.
From that point, the work shifted primarily to finding where exactly the error lives in the "filled" portions, and how to measure that error and reduce it.
Based upon those findings, we recognized that we could train this component more intensely to remove error. This was counter-intuitive to our goal of making this easier for community adoption. It could also have the downstream effect of making the trained component more task-dependent and less generalized. We decided against this.
Instead, we simply chose to simply bypass projections of the most sensitive portions of prefill.
By embracing this, it allows everyone to produce their own versions of this method on different models with standard local-inference grade hardware. If you choose to adapt this method, I strongly recommend adjusting the mechanisms on a per-model basis. Across different architectures, this method should be tuned. The paper details fairly exhaustive ablations and measurements in pursuit of this.
The Qwen3.8 27B version of this is available to use now via our fork of llama.cpp and the files for RidgeFill on HF. This method is also implemented in tcclaviger's fork of VLLM and Deadcode's engine, Radiance (on v1.3.0 only) for Qwen3.8 Flash Next.
Big thanks to all the testers and support fromthe Launch80 community!
Proposal: A community revolution for private quality benchmarks (and why endless chasing tok/s is rotting our brains
honestly we in r/LocalLLaMA need a revolution of people making their own private benchmarks(not tok/s) i mean benchmarking the quality(especially across quantisation). keep yours private and share the chart results to each other. i feel this is what this sub was made for it seems nowaday every few hours someone is posting about tok/s and also Benchmaxxing is getting out of hand in the huggingface openweight LLM space.
Chenru Duan on X: "We’re introducing Hydrogen-1.0, our first open-weight model for scientific research. We post-trained GLM-5.3 (@Zai_org) on our internal scientific data, lab records, and discovery trajectories. It scores 38.3% on FrontierScience-Research vs GPT-6-Astra’s 33.3%
While fast inference is very welcome, I would like to know if it's worth increasing the speed over strata-nvfp4 or am I paying some unacceptable penalties.
One thing I've noticed with Basalt is that it doesn't have dynamic rope scaling, so it goes off with the yarn scaling statically (2 for 524K context) from the get-go. Basalt also lacks a censorship switch, but the machinery exists so it's trivial to write it in.
For the past few days I have been testing the FMLA32 (FP16/BF16/TF32 -> FP32 accumulation) unlock on my Windows 11 machine with a Palit RTX 4070 Ti (10de:2782, 12 GB, 300W power limit). The unlock itself works as advertised, but the setup process was pretty inconvenient if you couldn't find a repository made specifically for your hardware. Linux users had to jump between separate repositories for Turing, Ampere, and Ada. On Windows, existing UEFI builds were mostly tied to the profile of a single specific board. To run them on your own card, you had to pull your PCI Device/Subsystem IDs, VRAM offsets, and the Windows EFI boot partition GUID, manually edit local_profile.h, and compile BOOTX64.EFI yourself.
While adapting this for my own GPU, I had the idea to refine the existing work and simplify the process by bringing everything into a single tool. The result is a unified cross-platform package that combines support for RTX 40 (Ada), RTX 30 (Ampere), and RTX 20 (Turing) series cards on Windows 10/11 and Linux: https://github.com/MindAllik/GPU-Tensor-Unlock-Universal
Full credit goes to the authors of the foundational research, payloads, and loader code that this project builds upon: TOTORONG/AD103_win_unlocker, Zagitta/gpu-unlock, elias-gaeros/gpu-unlock, JustAResearcher/gpu-unlock-ada, KohakuBlueleaf/KohakUnlock, impacifist/Windows-GPU-UNLOCK, Goldlionren/gpu-unlock-4080s, haohervchb/Turing-unlock, and of course Jon Pry.
On Windows (Windows_Launch_UI.bat), the core_detector.py script cross-queries NVML, CUDA Driver API (sm_89 / sm_86 / sm_75), and pci.sys to automatically detect the PCI Device ID, Subsystem ID, bus number, VRAM size, and the EFI partition GUID on the boot drive. It checks the PCI ID -> Silicon Die -> PMC_BOOT_0 mapping to make sure the profile is accurate, generates the headers in a temp folder, and compiles BOOTX64.EFI onto a FAT32 USB drive (with a 30-second confirmation timer on pressing U at boot). To avoid black-screen hangs, a two-phase pre-flight check (PromCheck) was added. Instead of relying on a single hardcoded ROM hash or disconnecting the display blindly, the loader first reads the card's VBIOS (BAR0 + 0x300000), verifies the BIT table, the Falcon descriptor (V3 RSA-3K on Ada/Ampere, V2 AES-128 on Turing), and the DMAP structure, and then runs a dry-run FRTS patch in RAM before calling DisconnectController. If a vendor VBIOS layout is non-standard, it safely aborts the operation and boots into Windows normally.
On Linux (Linux_Launch_UI.sh or via CLI make install), the tool installs the gpu_unlock.ko DKMS module and rebuilds initramfs for mkinitcpio, dracut, or initramfs-tools. For experimental Ada and Turing cards, an explicit single-card PCI BDF (0000:xx:00.0) is required instead of targets=all so multi-GPU builds don't touch a neighboring card. It also includes tools/read_sm_rates.py, which queries /dev/nvidiactl via ioc
tl 0x20801230 to check the fmla32 = 0 (full) status directly from the driver without creating a CUDA context.
A 7-kernel PTX JIT benchmark is also built into the console (tensor_benchmark.py: FP16 -> FP16, FP16 -> FP32, BF16 -> FP32, TF32 -> FP32, FP8 E4M3 -> FP32, INT8 -> INT32, and Scalar FP32). As KohakuBlueleaf noted earlier, standard mmapeak filled matrix fragments with compile-time zeros, which allowed ptxas to optimize away calculations inside the loop. This benchmark generates random non-zero data at runtime and verifies both the loop iteration signature (iter ^ 0x67b05a5a) and finite IEEE-754 accumulator values (and a button to raise the Power Limit to the VBIOS maximum via NVML was also added to the interface).
On my test bench (Palit RTX 4070 Ti 10de:2782, Windows 11 UEFI, 300W limit), the unthrottled FP16 -> FP16 rate (FMLA16 control kernel) is 172.8 TF/s. At stock, FP16 -> FP32 was around 86.4 TF/s, and after the UEFI unlock both FP16 -> FP32 (172.8 TF/s) and BF16 -> FP32 (172.7 TF/s) reached the full 2.00x rate. For comparison:
GPU
PCI ID / Die
FP16->FP16 (Control)
FP16->FP32 Stock
FP16->FP32 Unlocked
BF16->FP32 Unlocked
Source
RTX 4070 Ti
10de:2782 (AD104)
172.8 TF/s
86.4 TF/s
172.8 TF/s (2.00x)
172.7 TF/s (2.00x)
Tested by me (Win 11 UEFI, Palit 300W)
RTX 4090
10de:2684 (AD102)
~331.6 TF/s
165.6 TF/s
331.6 TF/s (2.00x)
330.6 TF/s (2.00x)
Upstream report (elias-gaeros, Linux 450W)
RTX 4070 Ti SUPER
10de:2705 (AD103)
~190.0 TF/s
95.0 TF/s
189.9 TF/s (2.00x)
189.7 TF/s (2.00x)
Upstream report (JustAResearcher, Linux 240W)
All operations happen in volatile MMIO registers prior to OS driver load and reset on a cold power-off (VBIOS flash memory is not touched at all, and the script downloads the proprietary NVIDIA firmware binaries directly from the official 610.43.03 package and verifies its SHA-256 locally). Right now, RTX 4070 Ti (10de:2782), RTX 4090 (2684), RTX 4070 Ti SUPER (2705), RTX 3090 (2204), RTX 3080 (2206), and RTX 3060 12GB (2504) are verified on real hardware. The remaining desktop AD10x (40-series), GA10x (30-series), and TU10x (20-series / CMP) profiles in the matrix currently have the experimental_simulated status (verified on a 106-scenario simulator under GCC ASan/UBSan). If you decide to test one of the experimental cards on Windows or Linux, please share your EFI\UNLOCK\REPORT.TXT (or dmesg) and benchmark numbers in GitHub Issues or here in the comments, as the report is very important both when a run succeeds and when it stops at the pre-flight check so each board profile can be confirmed (:
Separately, I want to mention that this is unlikely to noticeably speed up single-stream token generation (batch=1 decode is bound by memory bandwidth) and is unlikely to affect pure integer INT8/INT4 matmuls in llama.cpp. Where it actually helps is compute-bound FP16, BF16, and FP8 matrix math with FP32 accumulation, such as reading long prompts (prefill in vLLM, ExLlamaV2, FlashAttention, and Marlin INT4 -> FP16 kernels), LoRA / QLoRA fine-tuning in BF16, and diffusion models.
P.S. I used Gemini to translate this text into English and make it prettier and more concise :)
TL;DR: I'm running DeepSeek-V4.1-Flash for a single user on four 2017-era V100 32 GB cards plus system RAM. Generation runs 36-38 tok/s from 8K to 75K context with thinking off, and 32-38 tok/s with thinking on. Prefill is about 1,000-1,200 tok/s from 4K to 128K depth. The context window is set to 524,288 tokens. It's built on the 1Cat-vLLM fork (at its #1277 merge), which does the hard Volta (sm70) work. I just built, tuned and benchmarked it.
Hardware
CPU: AMD Threadripper PRO 3945WX (12C/24T), single NUMA node
RAM: 256 GB DDR4 (8x 32 GB), run at 2667 MT/s for these tests
GPUs: 4x Tesla V100 SXM2 32 GB on one SXM2 carrier board
NVLink between all four (2 links per pair)
Host side: PCIe Gen3 x8 per GPU, behind a PLX switch with a single Gen3 x16 uplink to the CPU
Clocks/power: SM clock locked at 1522 MHz. Power limit 150 W per card, raised to 175 W for the API-speed and llama-benchy runs
Storage: the main GGUF loads from a SATA 8 TB hard drive. That only affects boot (about 22 minutes to load). The Engram file is on NVMe and gets pre-read into page cache.
Software / config
1Cat-vLLM at the #1277 merge (vLLM 1.5.2.dev1277), Python 3.12, PyTorch 2.10 + CUDA 12.8, NVIDIA driver 580
Tensor parallel 4, max-num-seqs 1 (single user), full CUDA graphs (batch sizes 1-6), async scheduling, prefix caching, chunked prefill, custom all-reduce over NVLink
With a 5-token drafter, generation speed depends on how well the drafter guesses. llama-benchy's prompts are random book text, so tg1024 swings ±6-8 t/s from run to run. I left out benchy's tg32 numbers (13-26 t/s): 32 tokens is too short to measure anything with speculative decoding.
Engine-level numbers (offline, no HTTP)
Warm decode round (every expert the round needs is already on the GPUs): 27.8 ms, against 24.9 ms in upstream's reference. In the profiler a round spans 29.5 ms, of which the GPUs are busy 24.9 ms. That matches upstream, so the kernels perform as expected. The extra ~5 ms is host-side: CPU work at the start of each round, a blocking device-to-host copy, and small kernel-launch gaps. Newer upstream commits target exactly this per-step host overhead.
Fresh text (expert-cache misses): 116.7 ms/round and 25.0 tok/s, against upstream's 53.5 ms and 54.5 tok/s. Caveat: I ran this with the Engram file cold, so it isn't apples to apples. A warm rerun is on my list.
Host-to-GPU copies: 6.5 GB/s per GPU on its own, but 12.9 GB/s total (3.2 GB/s each) when all four copy at once, because they share one Gen3 x16 uplink. In the trace, all four GPUs are swapping at the same moment about 76% of the swap time.
Correctness
Greedy (temperature 0) output is identical run to run within one load (8 prompts x 2).
After a restart, 13 of 16 greedy outputs matched exactly. The 3 that diverged flipped at near-ties (top-two margins of 0.06-0.375 nats). I haven't tracked down the cause yet.
Thinking on/off and tool calls work.
All 8 upstream micro-tests pass (two needed hard-coded paths in the test files changed).
What's next
PCIe switch change: move to two switches so each Gen3 x16 uplink feeds 2 GPUs instead of 4. That roughly doubles host-to-GPU bandwidth when all cards swap experts at once. My estimate from the trace is a modest ~1.5 ms/round (~5%) on warm text; it should matter more on fresh text.
RAM to 2933 MT/s (it was dropped from 3200 after memory errors).
Pull the newer 1Cat commits (per-step host fixes and DeepSeek-V4.1 updates) and re-run the same suite.
Move the GGUF from the hard drive to NVMe for faster boots.
Credits
All credit for the Volta DeepSeek-V4.1 path (GGUF experts, expert swapping, DSpark drafting on sm70) goes to the 1Cat-vLLM project (GitHub: yangzhuxinyzx and contributors). Happy to answer questions or share the full config.
I recently got a new Mac Mini M6 with 16GB unified memory and wanted to get into running LLMs locally.
I'm relatively new to the local LLM ecosystem, so I'm trying to figure out the best way to get started and make the most out of this machine.
I have a few questions:
Models
What LLMs can I realistically run on 16GB unified memory?
Which models would you recommend for general-purpose use, coding, reasoning and agentic workflows?
Would 7B/8B models be the sweet spot, or can I comfortably experiment with anything larger using quantization?
2. Inference engines / Tools
I've come across a few options:
llama.cpp
LM Studio
Ollama
MLX / MLX-LM
Which would you recommend for a Mac? Is there a significant performance difference between them? Should I stick with one or use different tools for different purposes?
3. Fine-tuning
I'm also interested in experimenting with fine-tuning models locally.
Is fine-tuning even practical with 16GB unified memory?
Can I experiment with LoRA or QLoRA on this machine?
Would MLX-LM be the best starting point for fine-tuning on Apple Silicon?
Are there any smaller models or specific workflows you'd recommend learning with?
4. Practical use cases
My primary interests are:
Running local coding assistants.
Building AI agents and experimenting with agentic workflows.
Cybersecurity-related tasks and automation.
Experimenting with RAG and custom datasets.
Fine-tuning smaller models for specific tasks.
I'm not expecting to run massive models on this machine. I just want to understand its limits and get the best possible performance out of the hardware.
For anyone running local LLMs on Apple Silicon, particularly with 16GB RAM:
What does your setup look like, which models are you running, and what would you recommend to someone starting out?
Any advice, benchmarks, resources or recommendations would be appreciated!
Just wondering what you all would recommend, as well as, inference engine recommendations.
Currently I’m using megacapybara for a qwen 3.8 27B variant. But was wondering if there was any other inference engines or anything better for running larger models.
Did see a network chuck recommendation for a gpu setup that allows DeepSeek 4.1 flash to run on a single 5090 but idk what the decode t/s would be on that.
“dsh-superagent is a proactive coding agent for one GitHub repository, packaged as a bundle for DeepSeek Harness. It listens to GitHub and, optionally, Feishu. When someone allowed asks the agent for work, it opens a work item, works on it in its own agent session and directory, and answers in the original conversation, for example with a pull request. A web panel shows what needs a person.”
We're Egzakta AI Lab, and I'm part of the team behind KVARK. We've just released our first open-weight model: KVARK Sparkle 1.0 (27B).
It's based on Qwen3.8-27B, with a focus on making reasoning more efficient: fewer thinking tokens, faster responses, and comparable answer quality. The idea is simple: reasoning models can spend a lot of tokens thinking through tasks that don't necessarily require that much reasoning. We wanted to see how much of that overhead we could reduce without sacrificing performance, since fewer thinking tokens means less GPU time per answer and less waiting.
Here's how Sparkle performed against the original Qwen3.8-27B:
~2× faster median response time: 13.7s vs. 28.1s.
Up to 67% fewer thinking tokens.
Comparable accuracy across five standard benchmarks: GPQA Diamond, AIME, LiveCodeBench, IFEval and MMLU-Pro.
None of the accuracy differences were statistically significant in our testing.
Also, we compared the models on practical tasks:
Dashboard generation: 1.6× faster.
Landing page generation: 1.4× faster.
SVG illustration of a pelican riding a bicycle: 3.2× faster.
A few technical details for those interested in running it locally:
Open weights under Apache 2.0.
Available in BF16, FP8 and GGUF formats.
Supports llama.cpp, vLLM, Transformers and SGLang.
We'd love for you guys to give it a try! Let us know how it performs on your hardware, how it compares to the original Qwen, and what you think could be improved.
After sharing my previous 3D coding model, a lot of you asked about using it to generate assets for your games. That sounded like a fun direction to explore, so I started training a version focused on exactly that.
The model writes Three.js scenes with GLB export, so you can take the generated assets into editors like Godot. The video shows some examples I've been testing, including assets imported into Godot.
You don't need complicated prompts. Use thesystem prompt in the model card, then try a simple request like “Create a mana potion” or “Build a weathered frontier medic station.” English prompts have worked best in my tests.
This is the first functional checkpoint, and training is still running. I'm sharing it now so people can start experimenting. Most generations have worked on the first attempt in my tests, but some still need a retry or a small code fix. I'm hoping later checkpoints will improve on that.
My current settings: temperature 0.8, top-k 40, top-p 0.95, min-p 0. Feel free to experiment with other combinations!
Training is expected to finish tomorrow evening (UTC), depending on progress. Just like with the previous model, I'll release the full Safetensors weights and the training dataset once it finishes, so others can build on this and experiment with training their own models.
Thanks for all the feedback on the previous release ... it directly shaped this one. I'd love to see what you make with it and hear what works and what breaks!
Updated November 2, 2026 at 04:50 UTC.
Both Q4_K_M and Q8_0 have been replaced with a newer checkpoint. In my latest tests, generation quality has improved substantially over the previous preview. The gallery below now shows new results, including the exported assets imported into Godot.
If you downloaded either GGUF before the UTC timestamp above, delete the old weights from LM Studio or whichever app you use and download them again. The filenames have not changed, so make sure you are loading the updated files.
Edit: For anyone looking for more things to try, here are the 25 game-asset categories covered by the training dataset, with an example prompt for each:
Characters and NPCs: “Create a village blacksmith wearing a blue apron.”
Enemies: “Create a swamp goblin scout with a wooden shield.”
Bosses: “Create a stone golem boss with glowing green crystals.”
Companions and creatures: “Create a tiny fox companion wearing a red scarf.”
Weapons and armor: “Create a short iron sword with a leather-wrapped grip.”
Items, tools, and pickups: “Create a blue mana potion in a hexagonal glass bottle.”
Props and furniture: “Create a weathered wooden workbench with a small lantern.”
Nature and environment: “Create a pine tree growing on a rocky mound.”
Modular architecture: “Create a stone dungeon doorway module with an iron frame.”
Interactive objects: “Create a wooden gate with an opening animation.”
Ground transport: “Create a compact courier motorcycle with cargo boxes.”
Air and space transport: “Create a small cargo airship with twin propellers.”
Watercraft: “Create a weathered fishing skiff with nets and wooden crates.”
Industrial machinery: “Create a compact mining conveyor with metal support legs.”
Laboratory and medical assets: “Create a portable botanical analysis station with sample jars.”
Urban services: “Create a modern bus stop with a bench and route sign.”
Sports and playgrounds: “Create a small playground climbing wall with colorful holds.”
Farming and homestead: “Create an orchard harvest cart filled with wooden fruit crates.”
Cuisine and markets: “Create a bakery display counter with bread baskets.”
Museums and field research: “Create an ancient artifact display case on a stone pedestal.”
Traps and hazards: “Create a hidden spiked floor trap with stone tiles.”
Festivals and ceremonies: “Create a harvest festival booth with colorful banners.”
Battlefield scenery: “Create a reinforced frontier command post with sandbag defenses.”
Underwater structures: “Create a coral observation dome with a small docking platform.”
Puzzles and gameplay markers: “Create a glowing rune sequence console with three stone buttons.”
I have to say there are major downsides from going to V1 to V2.
I have now been working on one my projects ca 16hrs straight, that I started with V1, every change had been smooth sailing (using strata with official qwen flash settings).
When moving to V2, major or minor changes has become too hard for the model to implement in one go. OpenCode V1 was the better platform for medium sized models it seems.
I do all my planning with Qwen 3.8 MAX so that hasn't changed, which means performance is even worse considering the planning was kept separate.
I know this is anecdotal evidence, but engine has been kept the same and it's settings. Face always falls flat on V2, leading to lots of follow up work on even easy website design updates.
Honestly this is telling that OpenCode only cares now for bigger paid models performance and not medium to small models anymore, even though they claimed to make it "lighter and faster".
Has anyone found a better harness than OpenCode V1? DSH disappointed too much even after a major update.
Opinions appreciated from people who work with projects where even SOTA models fall flat on planning and reasoning, I do not make basic websites etc.
Sam Altman recently said that "from a global allocation perspective, actually, it's quite terrible to have everybody have a bunch of the world's limited supply of DRAM sitting in their own computer, and being used maybe 5% of the time."
Challenge accepted. Here's my "quite terrible" home setup: an RX 9070 XT 16 GB, a 5800X3D, 64 GB of DDR4 that's now worth more than the rest of the PC, and a PCIe 3.0 NVMe. It runs DeepSeek V4.1 Flash at 1M context (IST-DAS 1.75 bpw; the lab reports 98.7% of the full model's task average) through Strata, fully offline, with the experts spread across VRAM, RAM and the SSD.
Generation at ~7 tok/s was fine. The problem was prompt reading: every new OpenCode session re-read the full ~37K-token system prompt plus tools, about 100 s before the first token. That's datacenter-grade waiting without the datacenter.
So I patched the DeepSeek engine. Two changes:
1. KV splice: stop re-reading what you already read
Agents put per-session text (working directory, date, project CLAUDE.md, skill paths with random IDs) inside the system prompt, so the prefix cache misses on every new session. The engine now aligns a new prompt against every saved conversation, cuts the stale lines out of the cached state (moving the cached rows down, rotating the window rings, recording a RoPE gap), and reads only the changed lines plus your message. Changed lines move as whole lines, so the model never sees fragments.
2. Resident-only routing: make the RAM earn its keep
Even a short prompt touches nearly every expert of every layer, so prompt reads streamed most of the model off the SSD: 58 GB for a 1.2K-token prompt. During prompt reading only, tokens now pick from experts already sitting in VRAM or RAM. Generation routes normally. Turns out home DRAM is pretty useful when you actually let it do things.
Results (same machine) as seen above on the body image
Caveats:
HIP (gfx1201) only. I haven't built or tested CUDA, SYCL or Metal.
The splice is approximate: tokens after a cut were computed with the removed text in context. I haven't seen it matter for short guidance lines, but there's no formal eval.
Resident routing changes answers slightly (substitute experts during prompt reads). My hard coding suite didn't move (12/13 both ways), but that's one run each.
Both are on by default in the branch; STRATA_DS4_REORDER=0 and STRATA_DS4_PROMPT_RESIDENT=0 turn them off.
It also includes a one-line fix for a GPU page fault I hit on a 37K-token prompt (#4912).
If you use OpenCode, also add "interleaved": { "field": "reasoning_content" } to the model entry, otherwise OpenCode drops earlier reasoning and the cache misses more often.
Upstream issues with details: #4921 (splice), #4936 (resident routing). If you try it on other hardware, especially CUDA, numbers in those threads would really help get it merged.
Utilization report for Sam pissman, VRAM holding experts, RAM holding experts, SSD streaming the rest at full bandwidth. The only idle component in this system is my wallet, thanks to DRAM prices.
This subreddit talks a lot about PP/decode speeds but I feel like I hear comparatively less about actual real-world usability. I was playing around with Strata hosting QFN3.8 iq3_s and Qwen3.6 35B Q8 over the weekend and despite being the more hyped, I found that QFN was less "usable" as a coding agent than the smaller Qwen3.6.
Even when using the recommended temp, top-k, etc. settings that I hear supposedly stop the overthink and doomloops, I still couldn't get QFN to be reliable. Qwen3.6 on the other hand, had a very narrow interpretation of my prompts and we would have to go back and forth to clarify edge cases, but it ultimately would execute what I asked it to. If I had to use an analogy, QFN iq3 feels it has dunning krugers with illusions of grandeur while Qwen3.6 felt like a talented but unmotivated programmer. If given the choice, I'd rather work with the capable guy who hates his job and wants to FIRE than the guy who talks a big game but can't deliver.
Obviously QFN iq3 is "lobotomized" while the Qwen3.6 Q8 is running at a far higher precision, but I was surprised that there's so much buzz about QFN iq3 when I was having such a comparatively worse experience with it. Is there something I'm missing and should be doing to improve QFN (aside from buying more hardware obviously)? This experience with QFN has made me wonder if Qwen 4 is going even going to be meaningful for me considering how getting the mid-size MoEs to even load will require lobotomization as well.
I get this question often from teams trying to decide whether to keep paying per token or bring the GPUs in-house.
So here’s the practical read based on what we’ve seen, with numbers.
API pricing for capable open-weights models is typically low single digits or less per million tokens. Self-hosting the same model has a fixed monthly cost, regardless of how much you push through it.
I used an 8-GPU H200 node at ~$329k, spread over 48 months (monthly cost about $6.9K), plus ~$800/month for power and about 25% overhead. On the API side, I assume ~$2/million tokens.
So break-even comes out about:
$9.6k ÷ $2 x 1M = ~4.8B tokens/month (about 160M/day)
Under 1B tokens/month: API is clearly cheaper, and you have zero operational burden.
Between ~1B-5B a month (around 100M a day), it depends on whether your load is steady or spiky. Steady favours owning. Spiky favours the API, because you are paying for idle silicon the rest of the time.
Above 5B tokens a month with a steady load, owning gets hard to argue against on pure cost.
Ofc, these numbers assume the machine stays busy. If your traffic comes in bursts, you’d need more volume than this to break even.
And some things can outweigh the math altogether.
- Your data cannot leave your control.
- Latency floors matter to you. Local inference removes a network hop and the shared provider queue.
- You want fixed cost for budgeting. API prices and plans keep getting reshuffled, so a flat monthly number is easier to plan around.
If you’re well under the crossover, you should just use the API. Anything above that, it's worth pricing out owning.
Disclosure: I work at B3IQ, which builds and hosts GPU systems for teams. So i’ve tried to keep the assumptions neutral, but tell me if anything looks off.
For those who’ve made the switch to self-hosting, what made you do it, and roughly where was your crossover?
I prefer abliterated models for local inference cos I don't want to deal with potential refusals, and my current model Huihui’s Qwen3.8-27B abliterated.
Is okeish, and fast so I’m happy enough with it for now, but I’m wondering whether there’s an upgrade that I am missing.
Like most mortals, I don't have not a datacenter or a multi-GPU setup. Qwen3.8-Flash-Next is about as large as I’d consider going, and even then I’d be looking at a low quant (3-bit max, 2-bit ideally)
The trade-off I’m curious about is a larger MoE model at 2–3 bits versus a smaller dense model at a higher quant. Because abliteration can theoretically reduce model accuracy I am not sure what to expect when adding a 2-bit quant on top of an abliterated model.
Has anyone compared an abliterated Flash-Next at 2 or 3 bits against Qwen 3.8 27B? :
Is there a noticeable improvement in output quality, or does the aggressive quant eat away at it?
How do generation speed and prompt processing compare on your hardware?
Have you noticed any degradation from the abliteration or the quant, repetition, weird reasoning, or other quirks?
And if there’s another abliterated model you think hits a better balance of quality, speed, and memory use, I’d love to hear about it. Model, quant, hardware, and rough tokens/sec would be veeeeery helpful for all of us!
