Qwen3.5 35B A3B needs ~21.0 GB VRAM. RTX A4500 20GB has 20.0 GB. With Q2_K quantization, expect ~21 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
8.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
8.2 tok/s
TTFT
23529 ms
Safe context
4K
Memory
28.7 GB / 20.0 GB
Offload
30%
This setup is broadly balanced for this model.
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 9.6 tok/s | 10976 ms | 4K |
| Coding | F | Too heavy | 8.2 tok/s | 23529 ms | 4K |
| Agentic Coding | F | Too heavy | 6.2 tok/s | 45356 ms | 4K |
| Reasoning | F | Too heavy | 8.2 tok/s | 27807 ms | 4K |
| RAG | F | Too heavy | 6.2 tok/s | 56695 ms | 4K |
How Qwen3.5 35B A3B (35B params) fits at each quantization level on RTX A4500 20GB (20.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 13.7 GB | Low | C50 |
Q3_K_S | 3 | 17.2 GB | Low | F0 |
NVFP4 | 4 | 19.6 GB | Medium | F0 |
Q4_K_M | 4 | 21.3 GB | Medium | F0 |
Q5_K_M | 5 | 25.2 GB | High | F0 |
Q6_K | 6 | 28.7 GB | High | F0 |
Q8_0 | 8 | 37.5 GB | Very High | F0 |
F16 | 16 | 71.8 GB | Maximum | F0 |
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升级选项
RTX 5090 32GB经济之选Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$1,999 MSRP
RTX PRO 4500 Blackwell 32GB最佳性价比Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$2,499 MSRP
RTX 5000 Ada 32GBNVIDIA 升级Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$4,000 MSRP
Yes, RTX A4500 20GB can run Qwen3.5 35B A3B at Q2_K quantization (Runs with offload (needs ~0.6 GB host RAM)). The recommended Q4_K_M requires 28.7 GB which exceeds available memory, but at Q2_K it needs only 21.0 GB. Expected decode speed: 21.1 tok/s.
Qwen3.5 35B A3B (35B parameters) requires approximately 28.7 GB at Q4_K_M quantization. On RTX A4500 20GB, it fits at Q2_K using 21.0 GB.
The recommended quantization is Q4_K_M, but on RTX A4500 20GB the best fitting quantization is Q2_K, which uses 21.0 GB.
On RTX A4500 20GB, Qwen3.5 35B A3B achieves approximately 21.1 tokens per second decode speed with a time-to-first-token of 9157ms using Q2_K quantization.
For coding workloads, Qwen3.5 35B A3B on RTX A4500 20GB receives a F grade with 8.2 tok/s and 4K context.
On RTX A4500 20GB, Qwen3.5 35B A3B can safely use up to 12K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-lmstudio-community--qwen3-5-35b-a3b-gguf-on-rtx-a4500-20gb" 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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