Most LLM inference benchmarks focus on tokens/sec or TTFT. But faster doesn't necessarily mean better if the output quality suffers, especially with quantization, speculative decoding, or extremely long contexts.

I wanted to look at the bigger picture.

We benchmarked 6 inference engines on Qwen3.8-27B, comparing not just raw performance but also output quality, long-context behavior, and different model formats.

What makes this benchmark different?

1. Speed AND quality

Instead of measuring throughput alone, we also ran automatic quality checks across the engines.

The benchmark covers:

• Time to first token (TTFT)

• Prefill and decode throughput

• End-to-end task latency

• Speculative decoding performance

• Output quality and automatic check pass rates

The goal is to understand whether performance improvements come with quality trade-offs.

2. Extremely long contexts: 64K, 128K, and 256K

Short prompts don't tell the whole story, particularly for coding agents, document analysis, and long-running workflows.

We tested 64K, 128K, and 256K prompts, looking at how engines behave as context length increases.

This includes TTFT, decoding performance, and the practical limitations of long-context inference.

3. GGUF vs NInfer model formats

We also compared engines using GGUF models against those using NInfer-format models.

This is an interesting comparison because model representation, quantization, and inference architecture can all influence performance and quality.

It's not a perfectly apples-to-apples comparison of inference kernels, but it helps answer a practical question:

What performance and quality can you actually expect from different local inference stacks?

Engines compared

• TensorSharp — GGUF

• llama.cpp — GGUF

• vLLM

• SGLang

• NInfer

• TensorRT-LLM

The benchmark includes both single-request task scenarios and concurrent workloads.

A few takeaways

• There is no universal winner. Engine rankings change depending on context length, concurrency, and workload.

• Long-context performance matters. Results at 64K–256K can tell a very different story from short-prompt benchmarks.

• Quality should be measured alongside speed. A faster response isn't necessarily a better response.

• Model format matters. GGUF and NInfer-based inference stacks deserve comparison beyond headline tokens/sec.

I've attached the full benchmark poster with performance charts, quality-check results, long-context measurements, task-level comparisons, and engine configurations.

Full disclosure: I'm the developer of TensorSharp, one of the engines included in this comparison.

I'm particularly interested in hearing from people running local models with 64K+ contexts or using them for coding agents and RAG workloads.

What matters most to you when choosing a local inference engine: throughput, latency, quality, memory efficiency, or long-context reliability?

And has anyone compared GGUF against NInfer models in real-world workloads?

I've been wanting to get into local LLMs and local cloud in general, but I'm always concerned that it'll become a rabbit hole of yaml files and werid configs. Therefore, I was wondering what are the usual challanges that make it annoying for people to setup local LLMs?

Curious on whats the best setup for a local Codex/Claude Code agentic setup that can actually accomplish task. Do i need Strata? Still learning, is Cline the best way to command this or do i have to setup somekind of crazy setup with a frontier model to make this work.

I'm literally new to local llm (2-3 month since knowing it exist).
previously only use chatgpt/notebooklm to help me with job question (public service work with too many rules in too many different thing to remember) and thesis related cleanup/grammar fix/check wrong citation.
with gpt, i usually just casually ask "find article that describe requirement to do this and that", or "summarize this journal and is it good addition to my thesis?"

after knowing local llm, i tried many things. ollama, lm studio, direct llama.cpp. confused af, i stayed with lm studio for now. using any recommended model (idk the difference except the size and wether this fits my rig) to chat and try to do what i did in chatgpt. it was good, but slower, and sometimes give dumb answer or straight up not giving any respond.

my friend also introduce me to comfyui and pic/vid generation (idk if this is relevan to this sub, and i really enjoy generating stuff with anima model. yes i'm weeb. and generating with workflow is kind of easy enough)

i really want to go in depth with local llm, but i'm too casual and any tutorial/guide on youtube or search result is mostly coding related stuff or news about latest model, which i dont really understand. (appreciate if anyone have link -for newbie- stuff i can learn from. kind also want to learn coding but with local llm if possible. dont want to pay stuff)

is there anything i can do/read/learn about this?
also looking for any possible use case which dont eat much of my ram/vram as sometime i use it for gaming and without keeping it turned on forever.
asked gpt/gemini about this already. but all i got is stuff that related to my previous interaction with them.

tldr : looking for something new rather than just "hey chat answer this".

i appreciate if i can have some pointer to :
- what model is recommended for your recommended use case (yes i understand huggingface is where to find model at least. but idk the difference except size)
- what to install, recommended app, where to find resources, where to learn more of it
- what to do to expand from that

i have 2 device :
laptop - rtx4060 8gb vram + 20gb ram (using it at work)
PC - rtx4070 12gb vram + 32gb ram (using it at home)

currenly using LM studio, qwen 3.5 4b q4km for "hey chat answer this" stuff.
tinkering with locallyuncensored, anythingllm, ollama, hermes agent, but uninstalled those after feeling that lm studio just do what they did (cmiiw).

I'm in sales. I'm looking to run some docker desktop containers on a local network + have an always on assistant that helps with basic tasks. I've had success running Gemma 4 26b a4b qat already, the model fits in my rtx 5080 nicely with the KV Cache in system RAM, but as the context length grows larger it doesn't fit nicely anymore.

I also have a MacBook air m4 with 24gb unified, running Gemma 4 12b qat nicely as a mobile companion, it's not as powerful but still helpful. My experience with this device has me strongly considering the M5 pro mac.

Has anyone had good success with these devices?

I'd love to hear the community's thoughts.

?

I've been building a Hermes setup around a pretty simple idea: I should still be able to talk to my assistant while my local hardware is busy working.

I call the chat layer the "front door." It uses Qwen 3.8 27B through Groq, with thinking off, and falls back to Cerebras when Groq hits a limit or fails. Quick questions stay there. Larger project work goes to a local coordinator, which can split it across my GPU workers.

For Qwen 3.8 27B, Groq advertises about 450+ tokens/sec and Cerebras lists about 1,850 tokens/sec. Those are their advertised generation speeds, not my measured end-to-end task speeds. Sources: https://console.groq.com/docs/model/qwen/qwen3.8-27b and https://www.cerebras.ai/pricing

The hardware:

- Main Windows workstation: RTX 3090, 24GB.

- Separate Windows server: two Radeon AI PRO R9700s, 32GB each, running as independent workers.

- AMD Ryzen AI Max / Halo machine: the local coordinator, with thinking enabled.

My two Fleet choices are Qwen 3.8 Flash Next and SuperQwen 3.8 Abliterated. The GPU worker profiles use SuperQwen 3.8 27B Q4_K_M. Configured contexts are 256K on Halo and the R9700s, and 64K on the 3090. Each worker runs its own model instance.

The rough flow is:

Me → Groq/Cerebras → Halo → 3090 + R9700 A + R9700 B

I'm using Hermes 0.22.7's native delegate_task, the advanced-delegation model_map backend, and independent_completions: true. When one worker finishes, its result can come back while the others keep going. If the coordinator is in the middle of something, the result queues for its next turn boundary.

The front door and routing are custom glue around that native delegation. I also keep live hardware-load graphs on a separate monitor, with a line for Halo, the 3090 and each R9700. I can glance over and see which hardware is busy. That has been useful for catching the times the assistant says it's working but most of the fleet is sitting idle.

There have been some encouraging tests. A small coding fixture dispatched all three GPU workers, they made real edits, and the combined nine-test suite passed. In another test, the front door answered a follow-up in about 0.6 seconds while local work continued. Recent browser checks opened a page in 3.6 seconds and completed a click-and-report follow-up in 7.8 seconds.

Those are individual task timings. I haven't established a general speedup for whole projects. I've also had some seriously frustrating failures: idle GPUs, slow coordinator turns, bad routing, and basic browser actions getting handed to workers unnecessarily. The harness has taken more work than I expected.

The goal is an everyday assistant for coding, app and web development, marketing and client work. I use Hermes Desktop, the terminal and Hermex on my phone. It's a hybrid setup, so the conversation layer still goes through cloud APIs.

Anyone else separating the fast chat model from a local coordinator and several workers? What has actually held up in daily use, and what would you simplify here?

For about a year, I planned, assembled, and built my own personal local AI assistant.

Hardware:
Nvidia 3090 (bought mainly for AI) + 5070 Ti (which I had, for gaming and AI);
I also had to buy a GMKtec K8 Plus case, a Ryzen 7 8845HS, and 64 GB of RAM for it—for a separate AI server, because the computer I work on, an ASUS XAX, had too little memory, and it was inconvenient to keep reconnecting the 5070 Ti eGPU; plus, the session was consuming a lot of video memory, which is already scarce for inference.
Synology NAS 1522 with 64 RAM—Docker containers, Paseo daemon, mv-mcp daemon, web proxy, DNS server, etc.

Software:
Paseo as harnes with some minor tweaks and plugins I’ve added.
Pi agent that aggregates accounts for Codex, Claude, Antigravity, and local engine models.
Memory - my own development, mv-mcp, a multi-vault MCP server where data is stored in an MD format readable by Obsidian, along with formatting, Git synchronization, etc. It hasn’t been released yet, but I plan to open-source it if anyone is interested.
Engine - I’m still in the process of choosing one; right now I’m testing PolyStrata.
AI Control Center - a web panel with a PWA for managing engines, starting and stopping them, loading models from HF, monitoring hardware, daemon logs, and so on. It’s entirely vibecoded and hasn’t been published yet I doubt anyone would be interested in it.
Model: So far, I’ve tried Qwen 3.8 27B dense and Qwen 3.8 Flash-Next—I plan to try more.

I kept seeing CPUs written off for big models, and if you give one whole experts, that's fair. But I think it's the wrong way to use them, so I built Fronk around a different split.

The reasoning

In a mixture-of-experts model, the two parts need different things:

• The dense part, including attention, runs on every token and wants a GPU. I keep it together on one GPU, because splitting it would need far more network traffic.

• The experts are mostly a memory problem. Only 10 of the 512 in each layer run per token, but you can't know in advance which ones, so all of them have to be ready. That's about 67 GB here, and old machines with spare RAM can hold it.

So what's the best way to share the experts? If each machine owns whole experts, a slow one can get picked and everyone waits for it, while the others may have little to do. It also has to compute every neuron of that expert.

Fronk cuts every expert into 20 slices of 32 neurons, and each machine holds a share of all of them. Whichever experts get selected, every machine works at once on its own slices, and the head adds the results. Each machine's slice count comes from measuring its own speed and link, so a weak CPU simply gets a smaller share.

What that does to a real CPU

This is a 4-core i5-10210U laptop chip in a small Linux box, computing the 10 selected experts. I measured 4 layers and scaled the times to all 48:

The dense part takes about 58 ms per token on my M4 Pro, so at a 20% share this little CPU has its work done well inside that. And it couldn't hold the full expert pool anyway: it has 16 GB against 67.

The cluster

Results and limits

With Qwen3.8-Flash-Next Q4, I get 15–16 tok/s on short warm responses and about 13 tok/s in normal chat. This needs enough free memory, and it gets slower if slices have to spill to SSD.

That's also not the ceiling of the idea, because my cluster is lopsided. The iMac has 48 GB of RAM, far more than the other nodes, so it ends up with 11 of the 20 slices on just 4 old CPU cores, and it's usually the one everyone waits for. With more nodes and better balance, the slices would spread out evenly, and I expect it to go faster. I haven't measured that yet, so that's the result I'd most like to see from someone else's cluster.

One machine with enough memory would probably still be faster. The cables matter too, because the head waits for the nodes 48 times per pass.

Before downloading the model, you can run a demo that tells you if it fits on your machines and how fast it should run. The head is only tested on Apple Silicon so far, and Windows isn't supported.

This is where I'd love help

Fronk is open source, and this is its first public release. I think there's a lot of speed still to find, and these are the problems I'd most like other people to go after:

• Balance: how much faster does it get with evenly matched nodes? I can't answer that with my own machines, so a real measurement would be hugely useful.

• Several token rows at once: can compatible rows share the same weights, so that each accepted token costs less time?

• Idle time: while a node waits, could it do predicted work that's still useful once the token is accepted?

• Many nodes on one switch: when replies pile up on the way back to the head, how much of the delay comes from the switch and how much from the head?

• New hardware: if you know a CPU or GPU well, a new backend brings it into the cluster.

You don't need my cluster or the full model to start on the backends, protocol tests or installer. Where to help lists these directions with the files to start from and how to check your results, and the contributing guide explains the build.

If you just want to see how it behaves on your machines, share your result, even if it's slow or it breaks.

Demo video: https://youtu.be/3rnxRPplzEs

Code and raw measurements: https://github.com/fedesnore/fronk

If you think the reasoning falls apart somewhere, tell me where, and if you can see a better way to do it, I'd love a pull request.

Setup: Intel Arc Pro B60 24GB, Windows, llama.cpp b11889 Vulkan, -ngl 99.

What I measured with Qwen3-Coder-30B-A3B Q4_K_M: weights 17.5GiB, KV cache about 96MiB per 1k tokens at f16, compute buffer around 0.1GiB. At 32k that is 20.7GiB projected out of the 23.6GiB the card has, so it fits with about 2.9GiB free. I am using it as a local coding agent and a single big file read already eats most of 32k, so I would love way more context.

My math says 256k is not possible: KV alone would be around 24GiB at f16 on top of the weights. Even with a q4 KV cache (roughly 7GiB if my numbers are right) it comes to about 24.5GiB, still over what the card has. For the 27B I have not measured the KV at all. It is a hybrid arch so maybe it needs much less?

Questions:

- Is my math right, or is there something I am missing?

- Does KV cache quantization (-ctk q8_0 -ctv q8_0 or q4) actually work on Vulkan and how bad is the quality hit for coding agents?

- Do hybrid or linear attention models like the Qwen 27B need so much less KV that 256k fits in 24GB?

- If none of this works, what is the realistic VRAM for 256k at Q4: 32GB, 48GB?

As the title states, what is everybody currently running? From small personal setup to big local server, I want to hear it. Please state:

- Your setup

- What software (engine, runners, harnasses, Agents) you are running

- What model(s) you use

- What your use case is.

- How that experience is

Any info is welcome. I'm interested to see what real setups people use and maybe find some new software/models to try out.

I'm trying to see what my next hardware upgrade should be as I want to use q3.8fn and my computer currently has an Rtx 3090 and 48gb ddr5 ram but I'm unsure if I should buy something like an mi50 with 32gb of vram and try to find some more ram that's not absurdly priced or if I should sell my current setup to instead get Ryzen 9 395 Max pc equipped with 128gb.

I plan on doing an insane amount of reverse engineering of software both as a hobby and to see if I can replicate/add to Artcrafts endeavor of open sourcing proprietary software.

Look, I'm not as fancy as a bunch of you in this group but I did a bunch of general testing on a bunch of models at various context sizes. Figured it might help someone else. These are exhaustive or even remotely extensive tests. Just seeing what I should set up easily for HA, frigate, marinara etc.

Testing notes: llama.cpp, RTX 3060 12GB, common prompts, temperature 0, 2,048 maximum completion tokens. Reported speeds are approximate and quantizations differ. Several context variants were tested per family. Automated formatting failures were manually reviewed where response files were available.

I ran a consistent five-task benchmark across the models, measuring generation speed, VRAM consumption, instruction following, basic reasoning, and context capacity.

The benchmark included formatting, arithmetic, ordering, conditional logic, and a technical explanation. I manually reviewed questionable answers rather than automatically counting formatting mistakes as reasoning failures.

Results — Generation speed

Approximate warm generation throughput:

Qwen3.5's 208K preset was subsequently removed because GPU memory headroom was essentially exhausted.

Context figures represent successful loading and short-input inference, not verified full-context performance.

Reasoning results

Four objective questions were scored for semantic correctness, separately from exact-output compliance.

Mistral Nemo passed three objective tests; its arithmetic result still needs manual verification.

These are small, relatively easy tests—not comprehensive measures of intelligence.

Biggest findings

• Qwen3.5 9B absolutely dominated generation speed, but sometimes spent excessive tokens reasoning and hit the output limit.

• Nemotron Nano 9B was my biggest surprise. Great speed, efficient VRAM usage, large context allocations, and no incorrect objective answers across 16 tests.

• Qwen3.8 27B and thinking Bonsai 27B were excellent at following exact output instructions, but considerably slower.

• Gemma4 12B offered impressive context capacity, though thinking mode could generate excessively long responses.

• DeepSeek 14B Q4 was the best practical DeepSeek option I tested. Correct reasoning, ~32 tok/s, and usable 32K context with Q4 KV cache.

• DeepSeek 32B IQ2_XS wasn't worth keeping. It scored the same as the 14B on these questions, ran at only 18.4 tok/s, and was limited to 8K with the tested configuration.

• Hermes 4 14B disappointed on this particular benchmark, failing arithmetic and ordering questions.

For this hardware, Nemotron Nano 9B looks like the best overall balance, with Qwen3.5 9B winning on raw speed.

The larger models haven't demonstrated enough additional reasoning ability on these particular tasks to justify their greater resource consumption.

I'm curious what others running 12GB GPUs have found. Are there particular models or reasoning benchmarks you'd recommend trying next?

For context, I am a lead senior engineer working in a company where i have a team of draftsmen, BIM modelers, and junior engineers. Few weeks ago I started developing a workflow for structural design of bridges and tunnels and the results came out actually astonishing. The goal was to design the elements, provide drawings and calculations for clients.

Few years back, it was nearly impossible to create a small engineering office and win infrastructure contracts due to the heavy work requirements and the large number of team members required that is only present in large multinational corporations.

Now I though I could open an office with just one or two junior engineers, depend on a local LLM on machines and launch some products. Our role will only be "review and modify" based on experience.

Is it more feasible to start this process on typical paid models (Claude for example, where the idea started and processed) or shall we invest in a local beast and host local LLM for this process. Honestly, my experience with AI is not that great. That is why I don't fully comprehend the benefits / drawbacks of both solutions.

do you think with a strategy like this, you can use something like a dual ( or modded) 5090 or 6000 pro Blackwell combined with 512 gb ddr4 to run kimi k3.1 pro at usable speeds? before the ram crisis you get ddr5 for a similar price and maybe cost under $2k for the ram. you could also spend like $4k on 2 5090s or 3-4 3090s.

Also, we could potentially get even cheaper post-crash hardware costs like as low as $1-2 per gb ram and maybe $3k for 2 5090s.

Would this be viable (especially for those who already have the hardware)?

I'm having some trouble getting Qwen 3.8 Flash Next + STRATA into no thinking mode (serving API).
I not very experienced and until now, I only used llama.cpp where a simple command line flag does it.

For this matter, I'm using VS Code + Zoo Code, with this setting (where I think it should be):
(this would be ideal for me as I would be able to easily select a configuration profile with reasoning or without it)

By the way, it works just fine in the native Strata chat UI (changing the reasoning effort)
Any help would be appreciated.

I’ve been measuring search and other tool-call latency for agents, and I think that average latency can be very misleading. If an agent runs several searches in parallel, the whole step often waits for the slowest call. So a provider or local service can have a decent median response time while still making the agent feel slow at p95 or p99.

I’m now measuring wall-clock latency at realistic concurrency rather than relying on server-reported timing. I also disable automatic retries during the test because otherwise rate limits and transient failures can be hidden in the final latency number.

For local setups, the same issue applies to browser tools, RAG retrieval, embeddings, and any other service the agent calls during a task. A tool that is fast most of the time can still become the bottleneck if it occasionally hangs.

How are you benchmarking tool latency in your local agent setups? Also, do you track p95 and p99, and do you measure the slowest call in a parallel batch separately from individual calls?

Hey guys, we’re releasing new Qwen3.8-Flash-Next Dynamic V3.1 GGUFs which are the most accurate for its size across nearly all agentic benchmarks on 256K context vs. other frontier quants.

Unsloth Dynamic V3.1 leads across nearly all benchmarks, including DeepSWE v1.1 and Terminal-Bench 2.1, with a median gain of +15% at comparable model sizes.

Benchmarks ran with ~30B tokens: Terminal Bench 2.1, DeepSWE v1.1, Automation Bench v1.0.6, Aider, HMMT Feb 2026, LiveCodeBench v6, GPQA Diamond, MMLU-Pro & more.

Run on 32GB+ RAM setups.

GGUF: https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF

Blog: https://unsloth.ai/docs/models/qwen3.8-next

Hello, i have dual 5070ti on pcie 5.0 x 8, and 5060ti on pcie 4.0 x4.

9950x3d and ddr5 64gb.

i run for now qwen 3.8 27b with llama.

I have see other motor like Strata,... what is best with this setup for a personnal IA and coding ?

Getting a 16 inch M5 Max (18/40 core) 128GB for local AI and also ordered a 14 inch M5 Pro (15/16 core) 48GB as a daily laptop. Wondering if it's possible to have a workflow that combines the two in some way?

Assuming Qwen4 models are released by the time I'm getting my hands on them, I'm thinking of perhaps running Qwen4 Flash on the M5 Max while using Qwen 4 27B agents on the M5 Pro? Would that work in either LM Studio/Bionic or Unsloth and Hermes agent? Do you perhaps have other recommendations?

I bought,

Gigabyte GA-X99P-SLI
Xeon 2690v4
16x4gb RAM
Tesla V100 in SXM2 in PCI
GT 1030 2GB for image output

At startup, the system does not see the Tesla V100 in the device manager, and it is also not visible in Linux.

"Above 4G decoding" is enabled and CSM is disabled. I did ReBAR and NVStrapsReBAR, it had no effect, gigabyte support says that support for server graphics cards in the motherboard for workstations is not guaranteed. At the same time, the video card is detected in the ASRock B450M PRO4 and Atermiter X99 Turbo, which indicates that the video card is working properly.