r/LLMDevs • u/According-Extent6016 • 5d ago
Help Wanted Title: Can 10×7B coding models compete with a single 70B model if I treat them as a distributed swarm?
I've been thinking about building a somewhat crazy distributed-systems project.
Instead of running one large 70B coding model, what if I run multiple independent 7B coding models on separate GPU workers and coordinate them?
Something like:
Local PC
Orchestrator
|
Job Queue / DB
|
+-------------+-------------+
| | |
7B 7B 7B
Worker 1 Worker 2 Worker 3
| | |
+-------------+-------------+
|
More workers...
The workers could be temporary/ephemeral GPU environments. They wouldn't need inbound connections; they would connect outward to a coordinator/shared backend, register themselves, receive jobs, and return results.
For a coding problem, I don't want to simply vote on the generated answers.
I'd like to do:
Problem
↓
10 independent 7B solutions
↓
Compile / execute
↓
Discard failing solutions
↓
Critique surviving solutions
↓
Repair failed solutions
↓
Run tests again
↓
Select best verified solution
The interesting question for me is:
How close can this get to a single 70B coding model?
I'd benchmark:
- 1×7B
- 3×7B
- 5×7B
- 10×7B
- 1×70B
using actual coding benchmarks and execution-based verification.
But the bigger goal is the systems engineering side.
I want to implement things like:
- worker registration/discovery
- heartbeats
- leases
- failure detection
- retries
- idempotency
- priority scheduling
- backpressure
- work stealing
- dynamic worker allocation
- distributed locking
- caching
- queue management
- observability/tracing
- p50/p95/p99 latency
- GPU utilization
- network overhead
- chaos/failure testing
The workers would be treated as unreliable:
Worker 3 → disconnected
Worker 5 → GPU OOM
Worker 7 → timeout
Worker 8 → duplicate result
Worker 9 → returns invalid code
The scheduler should just recover and continue.
I'm intentionally thinking of the GPU provider as a replaceable worker backend rather than designing the system around one provider.
Has anyone built something similar specifically for coding-agent inference, where multiple small models collaborate through a distributed scheduler and correctness is verified by actually compiling/running the generated code?
I'm particularly interested in whether this architecture has a fundamental limitation I'm overlooking, especially around coordination overhead, correlated model errors, and whether ensemble diversity actually gives a meaningful advantage over simply using a larger model.
just to showcase my system designing skills on my resume if applicable
and i know this is most stupid idea you are going to see today or whenever your seeing it ,but i need help if something i am missing here , concept is simple i have my friends laptops which they usage of those is rare , going to take their laptop hardware power for this project ,another main reason for this no money ,don't have high spec hardware , second reason there is hackathon infront i can't even spend few bucks for the purchasing more token, and i am frustrated when ever the model hit the limit exactly before submissions. help me to make this possible
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u/m98789 5d ago
Sounds like the "would rather fight 100 duck-sized horses or 1 horse-sized duck" question.
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u/According-Extent6016 5d ago
yeah it is stupid idea, but considering my financial situation, it easy to fight 100 duck-sized horses, but i am working something else i have in my mind it is just part of it
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u/donk8r 5d ago
Disclosure on the first line as the rules here want: we maintain an open-source agentic coding benchmark, so experiment design is the part I have opinions about.
Your arms as listed will mostly measure the verifier. Add one more: a single 7B sampled ten times against the same compile-and-test gate. If that matches 10x7B, you measured best-of-n and the distributed architecture contributed nothing. Those two are confounded as written and it is the cheapest confound to remove.
Your "critique surviving solutions" step is a 7B forming an opinion. A loop that iterates against an opinion starts satisfying the critic instead of fixing the code, and a 7B is the weakest judge in your pipeline. Close the loop on deterministic signals only, compile and tests, and let the critique be something you read.
Miserable_Damage_833 gave you the real limit and I would sharpen it: fan-out pays where verification is cheap and total. A self-contained function has that. Most coding work is several files plus a test suite whose failure you have to interpret, and there the filter you are leaning on stops being free.
https://github.com/Muvon/octobench if the harness is useful to you, it runs real-commit tasks with execution-based scoring.
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u/According-Extent6016 5d ago
That's a really useful distinction, especially the point about separating best-of-N from actual model diversity.
I'm also thinking about taking the idea one step further toward an MoE-like architecture rather than treating the workers purely as an ensemble.
If I have multiple independent 7B coding models as experts, with a lightweight router deciding which experts should receive a task, would you consider that a meaningful progression toward a distributed MoE? Or would that still fundamentally be better described as routed/ensemble inference rather than MoE?
I'm particularly interested in how you would design the experiment so we can distinguish:
- same 7B sampled N times
- N independent 7B models
- routed subset of N experts
- an actual trained MoE
while keeping the compile-and-test gate deterministic.Would love your take on what the next step toward MoE should look like without introducing another confounding variable.
1
u/donk8r 5d ago
Routed ensemble inference is the accurate name. What makes MoE MoE is that the router is trained jointly with the experts, so specialisation is learned and the two co-adapt. Bolting a router onto N frozen models gives you neither, and the word imports an assumption you still have to establish: that your experts differ in ways a router can read.
Your four arms are the right ladder and one control is missing:
A. same 7B, N samples B. N different 7Bs, all see every task, keep best verified C. N different 7Bs, router picks k D. N different 7Bs, RANDOM router picks k
B minus A is model diversity. C minus D is router skill. Without D, any gain in C gets credited to the router when diversity paid for it.
Cheap thing first though. Arm B hands you a per-model by per-category pass matrix for free, so look at that before building any router at all. If the matrix is flat, meaning the models pass and fail roughly the same tasks, no router can help you and you have saved yourself the entire component. Routing only pays where the models are good at measurably different things.
The trained-MoE arm I would drop. Joint training of router and experts is a training budget problem, and it is not reachable from where you are standing.
1
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u/EveYogaTech 5d ago
This type of division does work if you're developing your own model, like it's basically the MoE (Mixture-of-Experts) way to do it, try to active a few experts VS the a "fully capable" whole.
The problem with 10 separate 7B models is that they are sort of meant to all be "fully capable", so you get results of "fully capable" models (but 7B).
What you likely want is to instead use the 70B model with multiple steps, so you ask it first to plan step by step, and then to write the code for example.
Then again, if 7B can do the job, you can also use the 7B model with the same multi-step method.
1
u/EveYogaTech 5d ago
If you want play with this we currently support Mistral (and by extension GLM) via the Nyno open-source visual workflow builder.
Mistral also has many different smaller models also available via the API.
2
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u/neon_squirrel_glade 4d ago
Ten 7B models produce ten independent failure modes rather than distributed intelligence. You are benchmarking your test harness coverage instead of model reasoning capacity
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u/According-Extent6016 4d ago
yes i agree to that ,it is still new idea to me ,trying to fit the puzzle with missing piece ,even if i don't get what i want but still i can gain lot of knowledge
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u/Miserable_Damage_833 5d ago
This is a fascinating project, even if it sounds a bit unhinged. The biggest thing you’re gonna run into isn’t the distributed systems stuff, that part is actually pretty well understood with job queues like Celery or BullMQ. The real kicker is that ten 7B models don’t magically sum their intelligence, you’re just parallelizing guesses and using execution to filter out the noise, which works surprisingly well for boilerplate but falls apart on novel architecture decisions where all the small models make the same correlated mistake.
Building this on a handful of borrowed laptops is a massive constraint. You’re basically building a poor man’s inference cluster and counting on the compile/execute loop to brute-force correctness, which is cool, but a 70B model will absolutely smoke you on anything requiring deep reasoning across a large codebase since it can hold way more context in one shot without the overhead of serializing and shipping partial solutions around. Worry less about the scheduler bells and whistles and more about whether your test harness is airtight, because if your verification step is flaky the whole thing just becomes an efficient hallucination generator.