RTX 3060 12GBBudget pick12 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.48.7 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$329 MSRP
Qwen3 8B DeepSeek v3.2 Speciale Distill needs ~7.8 GB VRAM. RTX 3070 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~90 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
89.7 tok/s
TTFT
2158 ms
Safe context
19K
Memory
7.8 GB / 8.0 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 | 89.7 tok/s | 1177 ms | 19K |
| Coding | C | Runs with offload | 89.7 tok/s | 2158 ms | 19K |
| Agentic Coding | C | Very compromised (needs ~0.4 GB host RAM) | 55.6 tok/s | 5061 ms | 19K |
| Reasoning | C | Runs with offload | 89.7 tok/s | 2551 ms | 19K |
| RAG | C | Very compromised (needs ~0.4 GB host RAM) | 55.6 tok/s | 6326 ms | 19K |
How Qwen3 8B DeepSeek v3.2 Speciale Distill (8B params) fits at each quantization level on RTX 3070 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C54 |
Q3_K_S | 3 | 3.9 GB | Low | C53 |
NVFP4 | 4 | 4.5 GB | Medium | C53 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | C53 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Copy-paste commands to run Qwen3 8B DeepSeek v3.2 Speciale Distill on your machine.
Run
lms load hf-teichai--qwen3-8b-deepseek-v3-2-speciale-distill-gguf && lms server startUpgrade options
RTX 3060 12GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$329 MSRP
RTX 5070 12GBBest valueAdds memory headroom for longer context windows and future model growth.
~$549 MSRP
RTX 4070 Super 12GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
~$599 MSRP
Yes, RTX 3070 Ti 8GB can run Qwen3 8B DeepSeek v3.2 Speciale Distill with a C grade (Runs with offload). Expected decode speed: 89.7 tok/s.
Qwen3 8B DeepSeek v3.2 Speciale Distill (8B parameters) requires approximately 7.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Qwen3 8B DeepSeek v3.2 Speciale Distill is Q4_K_M, which balances quality and memory efficiency.
On RTX 3070 Ti 8GB, Qwen3 8B DeepSeek v3.2 Speciale Distill achieves approximately 89.7 tokens per second decode speed with a time-to-first-token of 2158ms using Q4_K_M quantization.
For coding workloads, Qwen3 8B DeepSeek v3.2 Speciale Distill on RTX 3070 Ti 8GB receives a C grade with 89.7 tok/s and 19K context.
On RTX 3070 Ti 8GB, Qwen3 8B DeepSeek v3.2 Speciale Distill can safely use up to 19K 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-teichai--qwen3-8b-deepseek-v3-2-speciale-distill-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>
Preview: