RTX 5070 12GBGünstige Wahl12 GB VRAM (+1)672 GB/s (+188)
B
Raises estimated decode speed by about 48%.77.1 tok/s Dekodierung
Raises estimated decode speed by about 48%.
ca. $549 MSRP
Can Qwen3.5 9B run on GTX 1080 Ti 11GB?
YES — Runs Great
B55Good
Estimated from fit model
Qwen3.5 9B needs ~8.8 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~52 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
Operating mode
Choose the run profile you care about
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
Q4_K_M (Medium quality) — 8.8 GB, 52.0 tok/s, Runs well
8.8 GB required11.0 GB available
80% VRAM used
Fit status
Runs well
Decode
52.0 tok/s
TTFT
3722 ms
Safe context
49K
Memory
8.8 GB / 11.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime1.2 GB
Headroom1.1 GB
See how fast it feels
See how fast it feelsQwen3.5 9B on GTX 1080 Ti 11GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.
When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.
Key benefits of running locally:
- Full privacy: your prompts never leave your machine
- No per-token cost or rate limits
- Works offline once the model is downloaded
- Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:
Model size Q4 (4-bit) Q8 (8-bit) FP16
7B params ~4.3 GB ~7.5 GB ~14 GB
13B params ~7.9 GB ~13.9 GB ~26 GB
70B params ~42.7 GB ~74.9 GB ~140 GB
Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 52.0 tok/s decode · 3.7s TTFT (warm) · 130 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 52.0 tok/s | 2030 ms | 49K |
| Coding | B | Runs well | 52.0 tok/s | 3722 ms | 49K |
| Agentic Coding | C | Tight fit | 52.0 tok/s | 5414 ms | 49K |
| Reasoning | B | Runs well | 52.0 tok/s | 4399 ms | 49K |
| RAG | C | Tight fit | 52.0 tok/s | 6767 ms | 49K |
Inference speed
Qwen3.5 9B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for Qwen3.5 9B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| RTX 5090 32GB | 32 GB | Q4_K_M | 126.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 126.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 125.9 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 119.3 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 111.3 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 101.4 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 84.5 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 80.1 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 68.9 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 68.3 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 68.3 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 43.7 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 43.3 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 40.1 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 35.2 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 23.4 | Offloads |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How Qwen3.5 9B (9B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C51 |
Q3_K_S | 3 | 4.4 GB | Low | C52 |
NVFP4 | 4 | 5.0 GB | Medium | C53 |
Q4_K_M | 4 | 5.5 GB | Medium | C53 |
Q5_K_M | 5 | 6.5 GB | High | C52 |
Q6_KBest for your GPU | 6 | 7.4 GB | High | C52 |
Q8_0 | 8 | 9.6 GB | Very High | F0 |
F16 | 16 | 18.5 GB | Maximum | F0 |
Get started
Copy-paste commands to run Qwen3.5 9B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "lmstudio-community/Qwen3.5-9B-GGUF" \
--hf-file "Qwen3.5-9B-GGUF-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade-Optionen
Hardware, die Qwen3.5 9B gut ausführt
RTX 5070 12GBGünstige WahlRTX 4070 Super 12GBBestes Preis-Leistungs-Verhältnis12 GB VRAM (+1)504 GB/s (+20)
B
Raises estimated decode speed by about 36%.70.7 tok/s Dekodierung
Raises estimated decode speed by about 36%.
ca. $599 MSRP
RTX 4070 12GBNVIDIA-Upgrade12 GB VRAM (+1)504 GB/s (+20)
B
Raises estimated decode speed by about 33%.68.9 tok/s Dekodierung
Raises estimated decode speed by about 33%.
ca. $599 MSRP
Frequently asked questions
Can GTX 1080 Ti 11GB run Qwen3.5 9B?
Yes, GTX 1080 Ti 11GB can run Qwen3.5 9B with a B grade (Runs well). Expected decode speed: 52.0 tok/s.
How much VRAM does Qwen3.5 9B need?
Qwen3.5 9B (9B parameters) requires approximately 8.8 GB of memory with Q4_K_M quantization.
What is the best quantization for Qwen3.5 9B?
The recommended quantization for Qwen3.5 9B is Q4_K_M, which balances quality and memory efficiency.
What speed will Qwen3.5 9B run at on GTX 1080 Ti 11GB?
On GTX 1080 Ti 11GB, Qwen3.5 9B achieves approximately 52.0 tokens per second decode speed with a time-to-first-token of 3722ms using Q4_K_M quantization.
Can GTX 1080 Ti 11GB run Qwen3.5 9B for coding?
For coding workloads, Qwen3.5 9B on GTX 1080 Ti 11GB receives a B grade with 52.0 tok/s and 49K context.
What context window can Qwen3.5 9B use on GTX 1080 Ti 11GB?
On GTX 1080 Ti 11GB, Qwen3.5 9B can safely use up to 49K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-lmstudio-community--qwen3-5-9b-gguf-on-gtx-1080-ti-11gb" 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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