Compatibility check
Can you run Gemma 2 27B Instruct on the RTX A6000?
Yes, with quantization. Gemma 2 27B Instruct does not fit the RTX A6000 (48 GB) in FP16, but it runs at INT8 (8-bit) using about 32.7 GB (68% 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 1.4 GB KV cache at 4,096tokens, against the card's 48 GB. Verdicts leave ~10% headroom for activations and fragmentation.
| Precision | Weights | KV cache | Total | % of 48 GB | Fit |
|---|---|---|---|---|---|
| FP16 / BF16full quality | 62.6 GB | 1.4 GB | 64 GB | 133% | No |
| INT8 (8-bit)near-full quality | 31.3 GB | 1.4 GB | 32.7 GB | 68% | Fits |
| INT4 (4-bit)GPTQ / AWQ / GGUF Q4 | 15.6 GB | 1.4 GB | 17 GB | 35% | 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 Gemma 2 27B Instruct
Cards where this model fits (at its best precision):
Models that fit the RTX A6000
Other popular models that run on this card:
Go deeper
Frequently asked questions
Can the RTX A6000 run Gemma 2 27B Instruct?
Not in FP16, but yes at INT8 (8-bit), where it uses about 32.7 GB versus the card's 48 GB.
How much VRAM does Gemma 2 27B Instruct need?
Approximately 62.6 GB in FP16, 31.3 GB in INT8, and 15.6 GB in 4-bit for the weights, plus a KV cache of about 1.4 GB at 4,096 tokens.
Does quantization let Gemma 2 27B Instruct fit on the RTX A6000?
Yes. Dropping to INT8 (8-bit) brings total usage to about 32.7 GB, which fits the 48 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 1.4 GB here; doubling the context roughly doubles that term, so long-context use can push a tight fit over the edge.