Qwen 3 235B A22B needs ~105.0 GB VRAM. RTX PRO 6000 Blackwell Server Edition 96GB has 96.0 GB. With Q2_K quantization, expect ~23 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
60.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
7.7 tok/s
TTFT
25202 ms
Safe context
4K
Memory
156.7 GB / 96.0 GB
Offload
40%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 7.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 7.8 tok/s | 13491 ms | 4K |
| Coding | F | Too heavy | 7.7 tok/s | 25202 ms | 4K |
| Agentic Coding | F | Too heavy | 7.4 tok/s | 38039 ms | 4K |
| Reasoning | F | Too heavy | 7.7 tok/s | 29784 ms | 4K |
| RAG | F | Too heavy | 7.4 tok/s | 47548 ms | 4K |
How Qwen 3 235B A22B (235B 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 | 91.7 GB | Low | F0 |
Q3_K_S | 3 | 115.2 GB | Low | F0 |
NVFP4 | 4 | 131.6 GB | Medium | F0 |
Q4_K_M | 4 | 143.4 GB | Medium | F0 |
Q5_K_M | 5 | 169.2 GB | High | F0 |
Q6_K | 6 | 192.7 GB | High | F0 |
Q8_0 | 8 | 251.5 GB | Very High | F0 |
F16 | 16 | 481.7 GB | Maximum | F0 |
Copy-paste commands to run Qwen 3 235B A22B on your machine.
Run
lms load Qwen3-235B-A22B-Instruct-2507 && lms server startOpciones de mejora
NVIDIA B200 180GBOpción económicaHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$30,000 MSRP
NVIDIA H200 141GBMejor relación calidad-precioHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 629%.
~$30,000 MSRP
NVIDIA H200 PCIe 141GBMejora NVIDIAHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 519%.
~$30,000 MSRP
Yes, RTX PRO 6000 Blackwell Server Edition 96GB can run Qwen 3 235B A22B at Q2_K quantization (Very compromised (needs ~7.9 GB host RAM)). The recommended Q4_K_M requires 156.7 GB which exceeds available memory, but at Q2_K it needs only 105.0 GB. Expected decode speed: 23.1 tok/s.
Qwen 3 235B A22B (235B parameters) requires approximately 156.7 GB at Q4_K_M quantization. On RTX PRO 6000 Blackwell Server Edition 96GB, it fits at Q2_K using 105.0 GB.
The recommended quantization is Q4_K_M, but on RTX PRO 6000 Blackwell Server Edition 96GB the best fitting quantization is Q2_K, which uses 105.0 GB.
On RTX PRO 6000 Blackwell Server Edition 96GB, Qwen 3 235B A22B achieves approximately 23.1 tokens per second decode speed with a time-to-first-token of 8377ms using Q2_K quantization.
For coding workloads, Qwen 3 235B A22B on RTX PRO 6000 Blackwell Server Edition 96GB receives a F grade with 7.7 tok/s and 4K context.
On RTX PRO 6000 Blackwell Server Edition 96GB, Qwen 3 235B A22B can safely use up to 4K tokens of context at Q2_K quantization. The model's official context limit is 131K, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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