Qwen3.5 35B A3B needs ~29.9 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~22 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
Tight fit
Decode
21.6 tok/s
TTFT
8970 ms
Safe context
24K
Memory
29.9 GB / 32.0 GB
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 | C | Tight fit | 21.6 tok/s | 4893 ms | 24K |
| Coding | C | Tight fit | 21.6 tok/s | 8970 ms | 24K |
| Agentic Coding | D | Runs with offload (needs ~1.2 GB host RAM) | 14.3 tok/s | 19707 ms | 24K |
| Reasoning | C | Tight fit | 21.6 tok/s | 10601 ms | 24K |
| RAG | D | Runs with offload (needs ~1.2 GB host RAM) | 14.3 tok/s | 24634 ms | 24K |
How Qwen3.5 35B A3B (35B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.7 GB | Low | C49 |
Q3_K_S | 3 | 17.2 GB | Low | C50 |
NVFP4 | 4 | 19.6 GB | Medium | C49 |
Q4_K_M | 4 | 21.3 GB | Medium | C49 |
Q5_K_MBest for your GPU | 5 | 25.2 GB | High | C49 |
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 "unsloth/Qwen3.5-35B-A3B-GGUF" \
--hf-file "Qwen3.5-35B-A3B-GGUF-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade-Optionen
RTX A6000 48GBGünstige WahlRaises estimated decode speed by about 26%.
Adds memory headroom for longer context windows and future model growth.
ca. $4,650 MSRP
RTX PRO 5000 Blackwell 48GBBestes Preis-Leistungs-VerhältnisRaises estimated decode speed by about 145%.
Adds memory headroom for longer context windows and future model growth.
ca. $4,999 MSRP
NVIDIA L40 48GBNVIDIA-UpgradeRaises estimated decode speed by about 46%.
Adds memory headroom for longer context windows and future model growth.
ca. $5,500 MSRP
Yes, RTX 5000 Ada 32GB can run Qwen3.5 35B A3B with a C grade (Tight fit). Expected decode speed: 21.6 tok/s.
Qwen3.5 35B A3B (35B parameters) requires approximately 29.9 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 5000 Ada 32GB, Qwen3.5 35B A3B achieves approximately 21.6 tokens per second decode speed with a time-to-first-token of 8970ms using Q4_K_M quantization.
For coding workloads, Qwen3.5 35B A3B on RTX 5000 Ada 32GB receives a C grade with 21.6 tok/s and 24K context.
On RTX 5000 Ada 32GB, Qwen3.5 35B A3B can safely use up to 24K tokens of context. 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-unsloth--qwen3-5-35b-a3b-gguf-on-rtx-5000-ada-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: