Qwen 3.6 35B A3B needs ~40.1 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~329 tok/s.
Operating mode
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
Fit status
Runs well
Decode
329.1 tok/s
TTFT
588 ms
Safe context
262K
Memory
40.1 GB / 128.0 GB
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | S | Runs well | 329.1 tok/s | 350 ms | 262K |
| Coding | S | Runs well | 329.1 tok/s | 588 ms | 262K |
| Agentic Coding | S | Runs well | 329.1 tok/s | 856 ms | 262K |
| Reasoning | S | Runs well | 329.1 tok/s | 695 ms | 262K |
| RAG | S | Runs well | 329.1 tok/s | 1069 ms | 262K |
How Qwen 3.6 35B A3B (35B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.7 GB | Low | A81 |
Q3_K_S | 3 | 17.2 GB | Low | A81 |
NVFP4 | 4 |
Copy-paste commands to run Qwen 3.6 35B A3B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "Qwen/Qwen3.6-35B-A3B" \
--hf-file "Qwen3.6-35B-A3B-Q4_K_M.gguf" \
-c 4096 -ngl 99Your hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 123B | S | 37.5 tok/s | ||
| 122B | S |
Yes, Gaudi 3 128GB can run Qwen 3.6 35B A3B with a S grade (Runs well). Expected decode speed: 329.1 tok/s.
Qwen 3.6 35B A3B (35B parameters) requires approximately 40.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen 3.6 35B A3B is Q4_K_M, which balances quality and memory efficiency.
On Gaudi 3 128GB, Qwen 3.6 35B A3B achieves approximately 329.1 tokens per second decode speed with a time-to-first-token of 588ms using Q4_K_M quantization.
For coding workloads, Qwen 3.6 35B A3B on Gaudi 3 128GB receives a S grade with 329.1 tok/s and 262K context.
On Gaudi 3 128GB, Qwen 3.6 35B A3B can safely use up to 262K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/qwen-3.6-35b-a3b-on-gaudi-3-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| A81 |
Q4_K_M | 4 | 21.3 GB | Medium | A81 |
Q5_K_M | 5 | 25.2 GB | High | A82 |
Q6_K | 6 | 28.7 GB | High | A83 |
Q8_0 | 8 | 37.5 GB | Very High | A84 |
F16Best for your GPU | 16 | 71.8 GB | Maximum | S90 |
| 104.1 tok/s |
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.