Can One NVIDIA DGX Spark Support 4 Developers Running Independent AI Coding Agents for Unity Game Development?

Accelerated Computing DGX Spark / GB10 User Forum DGX Spark / GB10
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Hi everyone,

I’m a Game Producer planning to implement a private, locally hosted AI coding system for our game development company.

Our requirements:

  • 4 developers working simultaneously on 4 different Unity game projects.
  • Each developer uses an AI coding agent through VS Code or Rider.
  • One central DGX Spark (128 GB) hosting an open-source coding model.
  • Each developer needs independent context and access to their own project codebase.
  • AI should be capable of generating C# scripts, debugging, refactoring, understanding project architecture and assisting with development.
  • We may implement RAG and fine-tuning later.

I’d appreciate advice on the following:

  • Can a single DGX Spark realistically serve 4 simultaneous AI coding agents?
  • Which open-source coding model offers the best balance of performance and code quality on DGX Spark?
  • What tokens-per-second performance should we expect per developer under concurrent load?
  • What context window can we practically allocate to each developer (32K, 64K or higher)?
  • How much unified memory will the model and 4 concurrent KV caches consume?
  • Would you recommend Ollama, vLLM, llama.cpp or NVIDIA NIM for inference?
  • What’s the best architecture for connecting 4 development PCs to one AI server while keeping projects isolated?
  • Should we use RAG for Unity project knowledge or consider fine-tuning?
  • Is a single DGX Spark sufficient, or would a different GPU server be better for this workload?
  • Is there a cloud GPU environment where we can benchmark this setup before purchasing hardware?

Our goal is to build a reliable internal AI coding assistant that can help four developers work independently on different games.

I’d especially appreciate real-world benchmarks, recommended configurations and feedback from anyone running multiple coding agents on a DGX Spark.

Thanks!

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It is better if you yourself browse this subforum of DGX Spark GB10 and form your own opinion. That is my recommendation. You will gain 2 things: 1) real people using real systems and their real feedback 2) knowledge (which is tremendous treasure here) about how to setup any recipe/any setup that is available from community.

I did same thing for my use case of frontier research. Initially thought 1 DGX Spark unit will cut it. But reading and learning helped me understand the value of 2 DGX Sparks. After seeing how new recipes started dropping for new models (mid Jul-Aug), appetite came during the lunch and got another 2 to form ring of 4 sparks. Never regretted to this day about my decision. Learned a lot from the community knowledge.

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Hey azlanahmad, I’ll answer few of your questions with my current setup. I run Qwen3.8-Flash-Next QAD on Tensorfold currently.

Can a single DGX Spark realistically serve 4 simultaneous AI coding agents?

I often have 5 agents running simultaneously on a single spark, there are different setups that can serve more agents at once but keep in mind even though throughput increases with more agents, each stream will have worse tok/s.

Which open-source coding model offers the best balance of performance and code quality on DGX Spark?

Qwen3.8 Flash Next until Qwen4 is released

What tokens-per-second performance should we expect per developer under concurrent load?

Numbers vary depending on what each agent is doing, on my setup it ranges between 80-140 tok/s with 5 agents running, can be lower or higher depending on the tasks they are working on, speculative decoding acceptance and how much prefill needs to be interleaved while other agents are decoding are important factors

What context window can we practically allocate to each developer (32K, 64K or higher)?

I wouldn’t recommend below 200k for agentic coding, 32k or 64k can fill quite fast when agent is exploring the codebase.

How much unified memory will the model and 4 concurrent KV caches consume?

Depends on the model, engine and serving configuration. Usually only enough room is left in memory to be safe from going OOM to have a big kv cache pool

Would you recommend Ollama, vLLM, llama.cpp or NVIDIA NIM for inference?

I’d recommend forks of vLLM, SGLang and custom engines tailored for spark, which you can find on github and this forum. There are many setups that have different strengths and trade offs.

Is a single DGX Spark sufficient, or would a different GPU server be better for this workload?

Single spark likely isn’t sufficient for 4 developers assuming they will also run subagents. With a single spark you will also not be utilizing the ConnectX 7 that you will have paid for.

4

Hey @azlanahmad,

First, I’d recommend testing models on OpenRouter or any other API platform serving open-weight models (even Hugging Face Cloud Inference for popular ones like Qwen or DeepSeek). It’s the best way to probe their actual coding intelligence on your C# use cases beyond synthetic benchmarks before buying hardware.

To me, 1 DGX Spark is not enough, both in terms of model intelligence and compute for 4 people. As @leventc mentioned, a single developer sometimes runs up to 5 agents simultaneously, and your team will likely do the same. You’ll easily face 8+ concurrent requests (which is what a single power user generates on our setup). I’d say 4 DGX Sparks for 4 people sounds much more realistic: it gives you the memory and compute headroom to serve a far more capable model without constant head-of-line blocking during prefill.

  • Inference Engine: Use vLLM, it’s a no-brainer. Once you’re comfortable with it, you can always experiment with more bleeding-edge engines if you feel like it.

  • Context Window: A 1M context window is honestly amazing, but be careful, it requires careful tuning on a compact device like the GB10 because cold prefills scale super-linearly. Still, if you have the hardware headroom and tune your prefill thresholds properly, go as high as you can. (some people store cache on external fast drive I believe).

  • RAG vs. Context Engineering: For dev projects, traditional RAG is often over-engineered. Nowadays, it’s much more about context engineering, mostly driven by MCP (Model Context Protocol) and smart repo-searching tools. Depending on the sheer scale of your Unity codebases, look into how dev teams scaffold their workflows by wiring their dev tools directly into the agent’s context via custom MCP servers or well-structured Markdown instruction files (like .clinerules or CLAUDE.md).

  • Cloud Testing: You can easily rent cloud GPUs to benchmark throughput, and I even saw someone back in early January sharing a marketplace where people rent out their GB10 bandwidth online.

Feel free to check my thread on sharing GB10s with a dev team for real-world TTFT and concurrency numbers.

Cheers,
William

5

Now, I would have at least 1 box per full-time developer. These boxes and all the software and models are cheap.

6

I actively use AI coding agents for Unity game development with Unity CLI, Blender MCP, ComfyUI MCP, and custom MCP servers for search and deep research.

My main development environment is VS Code with Unity 6.3. I prefer Unity CLI over Unity MCP because I find it more convenient for AI agents to control the Unity Editor. The legacy Unity MCP server is being deprecated, but MCP support remains available through Unity CLI.

With a proper agent harness and version control, each developer can maintain an independent environment with separate or shared MCP servers.

To answer your questions:

  1. Four simultaneous agents are technically possible, but performance will likely be quite slow.

  2. As of November 5, 2026, I’d consider Qwen3.8-27B or Qwen3.8-Flash-Next

  3. I recommend at least 1,500 tokens/s for prefill and 35 tokens/s for decoding per developer. These are minimum recommendations, and higher speeds would be preferable.

  4. I personally recommend targeting 512K context for complex game development tasks. Smaller contexts tend to trigger frequent compaction, losing useful information. However, four simultaneous 512K contexts may be impractical on a single Spark.

  5. Spark Local Inference and vLLM can utilize most of the available memory. Actual consumption depends on model weights, KV cache configuration, and concurrency.

  6. I recommend Spark Local Inference if you’re unfamiliar with inference engines. I’ve also published a guide to simplify deployment:
    GitHub - NVIDIA/spark-local-inference · GitHub
    GitHub - gpdev-Pilcothink/flare-spark: flare-spark easy guide · GitHub

  7. I’m not entirely sure which aspect of the architecture you’re asking about. For hardware, DGX Spark or Mac Studio could be options. For project isolation, separate workspaces and MCP configurations should work.

  8. RAG would be useful if your agents need company-specific knowledge, such as internal coding conventions and project documentation.

  9. I don’t think a single DGX Spark would provide a comfortable real-time experience for four active developers. If budget allows, I’d recommend considering a DGX Station (GB300).

  10. Unfortunately, I’m not aware of a cloud provider offering DGX Spark instances for direct benchmarking.

7

Current open source models reach their heights via very high amount of thinking. For 1 token of out put it generates 10 thinking tokens as a safe rule. Plus tool calls, exploration, running tests. You need at least 40 t/s per developer simultaneously to start thinking about interactive work. Agents can wait people get frustrated. Comfort starts at 60-80 t/s.

Unless you developers are always stoned to the brim and don’t feel how time flies there is no way they can be satisfied with puny setup like this. Price/performance wise api/sub will keep beating local setup of comparable performance. You need other reasons to go local: privacy, curiosity, redteaming, security, not cost saving, unless main use is 24x7 agents, not humans who work 8x5 and require interactivity, demanding high specs while utilizing hardware to 25-30% on time basis.

I would agree that you need 4 sparks as a minimum. Or m5 max/ultra per person.

Depending on the model you need a lot if context nowadays. For example yesterday I did 5-6 sessions with deepseek V4. 1 1017 one after another filling to 600-800k tokens each on my 2 sparks plus side sessions using qwen 3.8 flash next on m5 max for light work with 512k max context. And deepseek sessions despite 70-90 t/s speed are hardly interactive, more planned batch work.

Start with api or subs, let them test and measure demand, token use, cost, then you have calculus

8

A single Spark isn’t a good fit. For dev, I’d want 2 sparks to run a larger model and even with that you’re really only going to support 1, maybe 2 devs. Beyond that, you still need an architect/designer via API.

You’d probably be much better off taking the 7k and paying for 4 Claude coder Max plans. That’d give you Fable for project architecture and design, Opus 5.5 for creating tasks and Sonnet writing code and running tests.

Alternatively, grab a FireworksAI/Baseten account and use Kimi K3.1 Pro for architect/design/project manager and Deepseek 4.1 Flash 1017 for a tester/coder/reviewer.