Qwen3.5 27B needs ~23.2 GB VRAM. RTX PRO 4000 Blackwell 24GB has 24.0 GB. With Q4_K_M quantization, expect ~34 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 with offload
Decode
34.3 tok/s
TTFT
5649 ms
Safe context
20K
Memory
23.2 GB / 24.0 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 | 34.3 tok/s | 3081 ms | 20K |
| Coding | C | Runs with offload | 34.3 tok/s | 5649 ms | 20K |
| Agentic Coding | D | Very compromised (needs ~1.5 GB host RAM) | 21.6 tok/s | 13049 ms | 20K |
| Reasoning | C | Runs with offload | 34.3 tok/s | 6676 ms | 20K |
| RAG | D | Very compromised (needs ~1.5 GB host RAM) | 21.6 tok/s | 16311 ms | 20K |
How Qwen3.5 27B (27B params) fits at each quantization level on RTX PRO 4000 Blackwell 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 10.5 GB | Low | C50 |
Q3_K_S | 3 | 13.2 GB | Low | C50 |
NVFP4 | 4 | 15.1 GB | Medium | C50 |
Q4_K_MBest for your GPU | 4 | 16.5 GB | Medium | C50 |
Q5_K_M | 5 | 19.4 GB | High | F0 |
Q6_K | 6 | 22.1 GB | High | F0 |
Q8_0 | 8 | 28.9 GB | Very High | F0 |
F16 | 16 | 55.4 GB | Maximum | F0 |
Copy-paste commands to run Qwen3.5 27B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "unsloth/Qwen3.5-27B-GGUF" \
--hf-file "Qwen3.5-27B-GGUF-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade-Optionen
RTX 5090 32GBGünstige WahlRaises estimated decode speed by about 113%.
Adds memory headroom for longer context windows and future model growth.
ca. $1,999 MSRP
RTX PRO 4500 Blackwell 32GBBestes Preis-Leistungs-VerhältnisRaises estimated decode speed by about 33%.
Adds memory headroom for longer context windows and future model growth.
ca. $2,499 MSRP
RTX 5000 Ada 32GBNVIDIA-UpgradeAdds memory headroom for longer context windows and future model growth.
ca. $4,000 MSRP
Yes, RTX PRO 4000 Blackwell 24GB can run Qwen3.5 27B with a C grade (Runs with offload). Expected decode speed: 34.3 tok/s.
Qwen3.5 27B (27B parameters) requires approximately 23.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3.5 27B is Q4_K_M, which balances quality and memory efficiency.
On RTX PRO 4000 Blackwell 24GB, Qwen3.5 27B achieves approximately 34.3 tokens per second decode speed with a time-to-first-token of 5649ms using Q4_K_M quantization.
For coding workloads, Qwen3.5 27B on RTX PRO 4000 Blackwell 24GB receives a C grade with 34.3 tok/s and 20K context.
On RTX PRO 4000 Blackwell 24GB, Qwen3.5 27B can safely use up to 20K 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--qwen3-5-27b-gguf-on-rtx-pro-4000-blackwell-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: