RTX 3060 12GB经济之选12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.36.4 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Can Qwen3.5 9B run on RTX 3070 Ti 8GB?
YES — With Offload
C53Usable
Estimated from fit model
Qwen3.5 9B needs ~8.2 GB VRAM. RTX 3070 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~56 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload
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) — 8.2 GB, 47.1 tok/s, Runs with offload (needs ~0.2 GB host RAM)
8.2 GB required8.0 GB available
102% VRAM needed
0.2 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
47.1 tok/s
TTFT
4106 ms
Safe context
12K
Memory
8.2 GB / 8.0 GB
Memory breakdown
Weights5.5 GB
KV Cache1.1 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsQwen3.5 9B on RTX 3070 Ti 8GB
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: 47.1 tok/s decode · 4.1s TTFT (warm) · 118 tok/s prefill
What limits this setup
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.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 67.0 tok/s | 1577 ms | 12K |
| Coding | C | Runs with offload | 56.1 tok/s | 3449 ms | 12K |
| Agentic Coding | C | Very compromised (needs ~0.8 GB host RAM) | 36.6 tok/s | 7695 ms | 12K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 47.1 tok/s | 4853 ms | 12K |
| RAG | C | Very compromised (needs ~0.8 GB host RAM) | 36.6 tok/s | 9619 ms | 12K |
Quantization options
How Qwen3.5 9B (9B params) fits at each quantization level on RTX 3070 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.5 GB | Low | C54 |
Q3_K_S | 3 | 4.4 GB | Low | C53 |
NVFP4Best for your GPU | 4 | 5.0 GB | Medium | C53 |
Q4_K_M | 4 | 5.5 GB | Medium | F0 |
Q5_K_M | 5 | 6.5 GB | High | F0 |
Q6_K | 6 | 7.4 GB | High | F0 |
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 99升级选项
能流畅运行 Qwen3.5 9B 的硬件
RTX 3060 12GB经济之选RTX 5060 Ti 16GB最佳性价比16 GB VRAM (+8)
C
Adds memory headroom for longer context windows and future model growth.50.6 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
RTX 5070 12GBNVIDIA 升级12 GB VRAM (+4)672 GB/s (+64)
B
Raises estimated decode speed by about 64%.77.1 tok/s 解码
Raises estimated decode speed by about 64%.
Adds memory headroom for longer context windows and future model growth.
~$549 MSRP
Frequently asked questions
Can RTX 3070 Ti 8GB run Qwen3.5 9B?
Yes, RTX 3070 Ti 8GB can run Qwen3.5 9B with a C grade (Runs with offload). Expected decode speed: 56.1 tok/s.
How much VRAM does Qwen3.5 9B need?
Qwen3.5 9B (9B parameters) requires approximately 8.2 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 RTX 3070 Ti 8GB?
On RTX 3070 Ti 8GB, Qwen3.5 9B achieves approximately 56.1 tokens per second decode speed with a time-to-first-token of 3449ms using Q4_K_M quantization.
Can RTX 3070 Ti 8GB run Qwen3.5 9B for coding?
For coding workloads, Qwen3.5 9B on RTX 3070 Ti 8GB receives a C grade with 56.1 tok/s and 12K context.
What context window can Qwen3.5 9B use on RTX 3070 Ti 8GB?
On RTX 3070 Ti 8GB, Qwen3.5 9B can safely use up to 12K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if Qwen3.5 9B feels slow on RTX 3070 Ti 8GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Embed this result▼
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<iframe src="https://willitrunai.com/embed/hf-lmstudio-community--qwen3-5-9b-gguf-on-rtx-3070-ti-8gb" 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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