The open-model question is really a matching problem
There is no single best open-source model. There is a best model for a job, and the gap between a good match and a bad one shows up directly in your latency, your cost per request, and the quality your users see. The good news for teams standing up their own inference: the strongest open-weight families all run on AMD Instinct GPUs today through ROCm, with vLLM as a first-class serving path on both MI300 and MI350 hardware, and AMD shipping day-zero support for major new releases like Qwen 3.5.
The AMD Instinct MI350X matters here for a specific reason. It carries 288GB of HBM3E memory per GPU, the largest high-bandwidth capacity in its class. The leading open models are large Mixture-of-Experts (MoE) architectures, and memory capacity is what decides how many GPUs it takes to hold one. A frontier MoE model that needs several GPUs on a 192GB-class part can consolidate onto fewer MI350X GPUs, which means less sharding, less traffic across the network fabric, and simpler, faster serving. Running these models on dedicated single-tenant MI350X clusters is exactly what Evergrid’s MI350X infrastructure is built for.
Here is how the major open families differ, and what each is actually good for.
DeepSeek: reasoning and cost-efficient scale
DeepSeek-V3.2 is a Mixture-of-Experts model with 671B total parameters and roughly 37B active per token, a 128K context window, and an MIT license. That architecture is the whole point: you get the knowledge capacity of a very large model while only activating a fraction of it per token, which keeps inference cost far below what a dense model of similar quality would demand. The companion reasoning model, DeepSeek-R1, is also MIT-licensed and tuned for step-by-step problem solving.
Best use cases: math, science, and logic-heavy reasoning; agentic workflows that need a strong planner; and high-volume general inference where cost per token has to stay low. The MIT license is unusually permissive for a model this capable, which makes DeepSeek a common default for teams that want commercial freedom without a restrictive weights agreement. On MI350X, the 671B parameter footprint is where the 288GB memory capacity earns its keep, since fewer GPUs are needed to keep the full expert set resident.
Qwen 3: the flexible workhorse, and the coding champion
Alibaba’s Qwen 3 family is the broadest range on this list, spanning small dense models from around 0.6B up to the 235B-parameter MoE flagship (Qwen3-235B-A22B, roughly 22B active) and the specialized Qwen3-Coder-480B (480B total, roughly 35B active). It ships under Apache 2.0, and AMD provides day-zero Instinct support for new Qwen releases through ROCm, vLLM, and SGLang.
The family splits into variants worth knowing: Instruct for general chat and instruction following, a Thinking variant tuned for extended reasoning, and Coder tuned for agentic software tasks like function calling, tool use, and reasoning across a whole repository.
Best use cases: Qwen3-Coder is one of the strongest open models available for agentic coding and long-context work over large codebases. The smaller dense Qwen 3 sizes are ideal when you need low latency, cheap serving, or on-device-class footprints. The mid and flagship MoE sizes cover multilingual general assistants and reasoning. If you want one family that scales from a lightweight classifier to a frontier coding agent under a permissive license, this is it.
Llama 4: native multimodality and enormous context
Meta’s Llama 4 herd introduced the first open-weight natively multimodal Llama models. Llama 4 Scout runs 109B total parameters with about 17B active across 16 experts and supports a context window up to 10 million tokens. Llama 4 Maverick runs 400B total with about 17B active across 128 experts and a 1 million token context. Both are open-weight under the Llama 4 community license, and both accept text and images natively rather than through a bolted-on vision adapter.
Best use cases: anything that mixes text and images, document understanding at scale, and workloads that need to reason over very long inputs, entire codebases, long transcripts, large document sets, or extended chat histories, without chunking and retrieval gymnastics. Scout’s efficiency and huge context make it a strong pick for long-document pipelines; Maverick balances long context with heavier reasoning. Note the license is a community license rather than a fully permissive one, so check the terms against your distribution plans.
gpt-oss: permissive reasoning with strong tool use
OpenAI’s gpt-oss models, gpt-oss-120b and gpt-oss-20b, are open-weight reasoning models released under Apache 2.0. They were built to punch above their size on reasoning and to be strong at tool use and agentic behavior, and the smaller 20b is light enough to serve cheaply while still handling structured, tool-driven tasks.
Best use cases: agentic systems that call tools and APIs, reasoning tasks where you want a permissive US-origin model, and deployments where the Apache 2.0 license and a manageable model size matter more than raw frontier scale. gpt-oss-20b is a practical default when you want reasoning quality without the memory and cost of a 400B-plus model; gpt-oss-120b steps up capability while staying far smaller than the frontier MoE models.
A quick way to choose
Match the job to the model before you match the model to the hardware:
- Reasoning, math, and cost-efficient general inference: DeepSeek-V3.2 or DeepSeek-R1.
- Agentic coding and large-repository work: Qwen3-Coder-480B.
- Multilingual assistants and flexible sizing: the Qwen 3 family, from small dense models to the 235B MoE.
- Multimodal input and very long context: Llama 4 Scout or Maverick.
- Tool-using agents on a permissive license and smaller footprint: gpt-oss-20b or 120b.
Every one of these runs on AMD Instinct through ROCm and vLLM. The larger the model, the more the MI350X’s 288GB memory changes the deployment math in your favor.
Why the infrastructure under the model decides the outcome
Picking the right model is half the work. The other half is where you run it, and that choice sets the ceiling on what the model can deliver. A metered public API abstracts the hardware away, which is convenient at low volume and expensive and opaque at scale. Running open weights yourself on dedicated capacity gives you control over three things a shared API cannot: latency, data isolation, and the ability to run the exact model and version you chose rather than whatever a provider decides to host.
That control only pays off if the underlying cluster is predictable. On shared, multi-tenant GPUs, your throughput moves with someone else’s traffic. On a single-tenant MI350X cluster, the fabric is yours, the performance you measure holds under load, and utilization stays high enough to make self-hosting cheaper than the API you left. We cover that math in understanding inference economics, and the tenancy argument in full in the case for the inference neocloud.
Frequently asked questions
Can you run DeepSeek, Qwen, Llama 4, and gpt-oss on AMD Instinct GPUs?
Yes. All four families run on AMD Instinct MI300X, MI325X, and MI350X GPUs through ROCm, with vLLM and SGLang as production serving paths. AMD provides day-zero support for major new open-model releases.
Why does the MI350X suit large open models specifically?
The MI350X carries 288GB of HBM3E memory per GPU with 8 TB/s of bandwidth, the largest high-bandwidth capacity in its class. Large Mixture-of-Experts models like DeepSeek-V3.2 (671B) and Qwen3-Coder (480B) need substantial memory to stay resident, and more memory per GPU means fewer GPUs, less sharding, and lower inter-GPU network traffic.
Which open-source model is best for coding?
Qwen3-Coder-480B is among the strongest open models for agentic software work: function calling, tool use, and reasoning across large repositories. For lighter coding tasks, smaller Qwen 3 dense models or gpt-oss deliver good results at lower cost.
Which open model is best for reasoning?
DeepSeek-R1 and DeepSeek-V3.2 are leading choices for math, science, and logic-heavy reasoning under an MIT license. gpt-oss models are strong reasoning options when a permissive Apache 2.0 license and a smaller footprint are priorities.
Which open model handles images and very long documents?
Llama 4 Scout and Maverick are natively multimodal and support context windows up to 10 million and 1 million tokens respectively, making them well suited to mixed text-and-image workloads and very long inputs.
What licenses do these models use?
DeepSeek-V3.2 and R1 are MIT, Qwen 3 and gpt-oss are Apache 2.0, and Llama 4 is released under the Llama 4 community license. Check each license against your commercial and distribution plans.
Talk to an engineer
Choosing a model is a workload question, and choosing where to run it is an infrastructure question. If you are deploying open-weight models in production and want them on dedicated AMD Instinct MI350X capacity, book a technical consult at connect@evergrid.ai. We will help you match the model to the job and the cluster to the model.
