Qwen3.5 27B needs ~23.2 GB VRAM. Tesla P40 24GB has 24.0 GB. With Q4_K_M quantization, expect ~12 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
12.4 tok/s
TTFT
15620 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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 | 12.4 tok/s | 8520 ms | 20K |
| Coding | C | Runs with offload | 12.4 tok/s | 15620 ms | 20K |
| Agentic Coding | D | Very compromised (needs ~1.5 GB host RAM) | 7.3 tok/s | 38393 ms | 20K |
| Reasoning | C | Runs with offload | 12.4 tok/s | 18460 ms | 20K |
| RAG | D | Very compromised (needs ~1.5 GB host RAM) | 7.3 tok/s | 47992 ms | 20K |
How Qwen3.5 27B (27B params) fits at each quantization level on Tesla P40 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 488%.
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 269%.
Adds memory headroom for longer context windows and future model growth.
ca. $2,499 MSRP
RTX 5000 Ada 32GBNVIDIA-UpgradeRaises estimated decode speed by about 126%.
Adds memory headroom for longer context windows and future model growth.
ca. $4,000 MSRP
Yes, Tesla P40 24GB can run Qwen3.5 27B with a C grade (Runs with offload). Expected decode speed: 12.4 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 Tesla P40 24GB, Qwen3.5 27B achieves approximately 12.4 tokens per second decode speed with a time-to-first-token of 15620ms using Q4_K_M quantization.
For coding workloads, Qwen3.5 27B on Tesla P40 24GB receives a C grade with 12.4 tok/s and 20K context.
On Tesla P40 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-tesla-p40-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: