Qwen3.5 35B A3B needs ~36.3 GB VRAM. RTX PRO 6000 Blackwell Server Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~63 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
62.8 tok/s
TTFT
3081 ms
Safe context
249K
Memory
36.3 GB / 96.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 62.8 tok/s | 1681 ms | 249K |
| Coding | C | Runs well | 62.8 tok/s | 3081 ms | 249K |
| Agentic Coding | C | Runs well | 62.8 tok/s | 4482 ms | 249K |
| Reasoning | C | Runs well | 62.8 tok/s | 3641 ms | 249K |
| RAG | C | Runs well | 62.8 tok/s | 5602 ms | 249K |
Inference speed
Estimated decode speed (tokens/sec) for Qwen3.5 35B A3B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~56 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 56.2 | Tight |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 28.1 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 28.1 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 26.1 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 21.7 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 20.6 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 17.7 | Tight |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 16.6 | Heavy offload |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 11.2 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 10.6 | Heavy offload |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 10.3 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 9.7 | Heavy offload |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 6.5 | Too big |
| RTX 4070 12GB | 12 GB | Q4_K_M | 2.7 | Too big |
| RTX 3060 12GB | 12 GB | Q4_K_M | 2.0 | Too big |
| RTX 4060 8GB | 8 GB | Q4_K_M | 2.0 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How Qwen3.5 35B A3B (35B params) fits at each quantization level on RTX PRO 6000 Blackwell Server Edition 96GB (96.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.7 GB | Low | C40 |
Q3_K_S | 3 | 17.2 GB | Low | C41 |
NVFP4 | 4 | 19.6 GB | Medium | C41 |
Q4_K_M | 4 | 21.3 GB | Medium | C41 |
Q5_K_M | 5 | 25.2 GB | High | C42 |
Q6_K | 6 | 28.7 GB | High | C42 |
Q8_0 | 8 | 37.5 GB | Very High | C44 |
F16Best for your GPU | 16 | 71.8 GB | Maximum | C48 |
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 "unsloth/Qwen3.5-35B-A3B-GGUF" \
--hf-file "Qwen3.5-35B-A3B-GGUF-Q4_K_M.gguf" \
-c 4096 -ngl 99Yes, RTX PRO 6000 Blackwell Server Edition 96GB can run Qwen3.5 35B A3B with a C grade (Runs well). Expected decode speed: 62.8 tok/s.
Qwen3.5 35B A3B (35B parameters) requires approximately 36.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3.5 35B A3B is Q4_K_M, which balances quality and memory efficiency.
On RTX PRO 6000 Blackwell Server Edition 96GB, Qwen3.5 35B A3B achieves approximately 62.8 tokens per second decode speed with a time-to-first-token of 3081ms using Q4_K_M quantization.
For coding workloads, Qwen3.5 35B A3B on RTX PRO 6000 Blackwell Server Edition 96GB receives a C grade with 62.8 tok/s and 249K context.
On RTX PRO 6000 Blackwell Server Edition 96GB, Qwen3.5 35B A3B can safely use up to 249K tokens of context. The model's official context limit is —, 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/hf-unsloth--qwen3-5-35b-a3b-gguf-on-rtx-pro-6000-blackwell-server-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: