NVIDIA H800 80GBコスパ重視80 GB VRAM (+16)3000 GB/s (+1362)
C
Raises estimated decode speed by about 118%.113.8 tok/sデコード
Raises estimated decode speed by about 118%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Can Qwen3.5 35B A3B run on AMD Instinct MI210 64GB?
YES — Runs Great
C51Usable
Estimated from fit model
Qwen3.5 35B A3B needs ~32.8 GB VRAM. AMD Instinct MI210 64GB has 64.0 GB. With Q4_K_M quantization, expect ~52 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
Operating mode
Choose the run profile you care about
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Q4_K_M (Medium quality) — 32.8 GB, 52.2 tok/s, Runs well
32.8 GB required64.0 GB available
51% VRAM used
Fit status
Runs well
Decode
52.2 tok/s
TTFT
3711 ms
Safe context
138K
Memory
32.8 GB / 64.0 GB
Memory breakdown
Weights21.3 GB
KV Cache4.1 GB
Runtime0.9 GB
Headroom6.4 GB
See how fast it feels
See how fast it feelsQwen3.5 35B A3B on AMD Instinct MI210 64GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 52.2 tok/s decode · 3.7s TTFT (warm) · 130 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 52.2 tok/s | 2024 ms | 138K |
| Coding | C | Runs well | 52.2 tok/s | 3711 ms | 138K |
| Agentic Coding | C | Runs well | 52.2 tok/s | 5398 ms | 138K |
| Reasoning | C | Runs well | 52.2 tok/s | 4386 ms | 138K |
| RAG | C | Runs well | 52.2 tok/s | 6747 ms | 138K |
Quantization options
How Qwen3.5 35B A3B (35B params) fits at each quantization level on AMD Instinct MI210 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.7 GB | Low | C42 |
Q3_K_S | 3 | 17.2 GB | Low | C43 |
NVFP4 | 4 | 19.6 GB | Medium | C44 |
Q4_K_M | 4 | 21.3 GB | Medium | C44 |
Q5_K_M | 5 | 25.2 GB | High | C45 |
Q6_K | 6 | 28.7 GB | High | C46 |
Q8_0Best for your GPU | 8 | 37.5 GB | Very High | C48 |
F16 | 16 | 71.8 GB | Maximum | F0 |
Get started
Copy-paste commands to run Qwen3.5 35B A3B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "lmstudio-community/Qwen3.5-35B-A3B-GGUF" \
--hf-file "Qwen3.5-35B-A3B-GGUF-Q4_K_M.gguf" \
-c 4096 -ngl 99アップグレードオプション
Qwen3.5 35B A3Bを快適に動かすハードウェア
Frequently asked questions
Can AMD Instinct MI210 64GB run Qwen3.5 35B A3B?
Yes, AMD Instinct MI210 64GB can run Qwen3.5 35B A3B with a C grade (Runs well). Expected decode speed: 52.2 tok/s.
How much VRAM does Qwen3.5 35B A3B need?
Qwen3.5 35B A3B (35B parameters) requires approximately 32.8 GB of memory with Q4_K_M quantization.
What is the best quantization for Qwen3.5 35B A3B?
The recommended quantization for Qwen3.5 35B A3B is Q4_K_M, which balances quality and memory efficiency.
What speed will Qwen3.5 35B A3B run at on AMD Instinct MI210 64GB?
On AMD Instinct MI210 64GB, Qwen3.5 35B A3B achieves approximately 52.2 tokens per second decode speed with a time-to-first-token of 3711ms using Q4_K_M quantization.
Can AMD Instinct MI210 64GB run Qwen3.5 35B A3B for coding?
For coding workloads, Qwen3.5 35B A3B on AMD Instinct MI210 64GB receives a C grade with 52.2 tok/s and 138K context.
What context window can Qwen3.5 35B A3B use on AMD Instinct MI210 64GB?
On AMD Instinct MI210 64GB, Qwen3.5 35B A3B can safely use up to 138K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-lmstudio-community--qwen3-5-35b-a3b-gguf-on-instinct-mi210-64gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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