Compatibility check
Can you run Qwen3 8B on the RTX 4070 SUPER?
Yes, with quantization. Qwen3 8B does not fit the RTX 4070 SUPER (12 GB) in FP16, but it runs at INT8 (8-bit) using about 10 GB (83% of VRAM). Use a GPTQ, AWQ, or GGUF build to get there, and keep prompts moderate to leave room for the KV cache.
Memory breakdown
Weights plus a 0.6 GB KV cache at 4,096tokens, against the card's 12 GB. Verdicts leave ~10% headroom for activations and fragmentation.
| Precision | Weights | KV cache | Total | % of 12 GB | Fit |
|---|---|---|---|---|---|
| FP16 / BF16full quality | 18.8 GB | 0.6 GB | 19.4 GB | 162% | No |
| INT8 (8-bit)near-full quality | 9.4 GB | 0.6 GB | 10 GB | 83% | Fits |
| INT4 (4-bit)GPTQ / AWQ / GGUF Q4 | 4.7 GB | 0.6 GB | 5.3 GB | 44% | Fits |
Planning estimates, not a substitute for profiling. Real usage varies with the inference runtime, batch size, and how much context you actually use — the KV cache grows linearly with prompt length.
GPUs that run Qwen3 8B
Cards where this model fits (at its best precision):
Models that fit the RTX 4070 SUPER
Other popular models that run on this card:
Go deeper
Frequently asked questions
Can the RTX 4070 SUPER run Qwen3 8B?
Not in FP16, but yes at INT8 (8-bit), where it uses about 10 GB versus the card's 12 GB.
How much VRAM does Qwen3 8B need?
Approximately 18.8 GB in FP16, 9.4 GB in INT8, and 4.7 GB in 4-bit for the weights, plus a KV cache of about 0.6 GB at 4,096 tokens.
Does quantization let Qwen3 8B fit on the RTX 4070 SUPER?
Yes. Dropping to INT8 (8-bit) brings total usage to about 10 GB, which fits the 12 GB card with headroom for the KV cache.
What happens to memory with longer context?
The KV cache grows linearly with prompt length. At 4,096 tokens it is about 0.6 GB here; doubling the context roughly doubles that term, so long-context use can push a tight fit over the edge.