Can the RTX A6000 48GB Run Llama 3.2 1B?
Runs
Yes. Llama 3.2 1B at Q4_K_M is a 0.8 GB download (Hugging Face file listing for bartowski/Llama-3.2-1B-Instruct-GGUF), and the RTX A6000 48GB has 48 GB of VRAM, leaving about 47.2 GB for the KV cache and runtime. No published speed measurement for this pair passes SpecPicks' sanity checks yet, so no tokens-per-second figure is quoted.
Buy the RTX A6000 48GB
No new listing for this card is tracked on Amazon today; it mostly trades used.
Published runs for Llama 3.2 1B on the RTX A6000 48GB
No published run for this pair has passed the checks yet.
More about this pair
How much VRAM does Llama 3.2 1B need?
The Q4_K_M file is 0.8 GB. Add roughly 1.5 GB for the runtime and an ~8K-token context, so about 2.3 GB of VRAM holds it comfortably. Longer contexts grow the KV cache and need more.
What is the fastest Llama 3.2 1B can generate on the RTX A6000 48GB?
Generation reads every weight once per token, so the RTX A6000 48GB's 768 GB/s of memory bandwidth divided by the 0.8 GB file sets a ceiling near 948 tokens per second. Real runs land below it; SpecPicks rejects any published figure above it.
Check another pair
Llama 3.2 1B on other GPUs
- RTX 4060 — Runs
- RTX 3060 Ti — Runs
- RTX 3060 12GB — Runs, 184 tok/s median
- Intel Arc B580 — Runs
- RTX 4070 SUPER — Runs, 183 tok/s median
- RTX 4060 Ti 16GB — Runs, 209 tok/s median
- RTX 5060 Ti 16GB — Runs, 210 tok/s median
- Intel Arc A770 16GB — Runs
- Radeon RX 9070 XT — Runs
- RTX 4070 Ti SUPER — Runs, 161 tok/s median
- RTX 4080 — Runs
- RTX 5070 Ti — Runs, 185 tok/s median
- RTX 5080 — Runs, 103 tok/s median
- RTX 3090 — Runs, 267 tok/s median
- Radeon RX 7900 XTX — Runs, 106 tok/s median
- RTX 4090 — Runs
- RTX 5090 — Runs
- RTX PRO 6000 Blackwell 96GB — Runs, 244 tok/s median
Other models on the RTX A6000 48GB
- Llama 3.2 3B — Runs
- Mistral 7B — Runs
- Qwen2.5 7B — Runs
- Llama 3.1 8B — Runs
- DeepSeek-R1-Distill-Llama 8B — Runs
- Qwen3 8B — Runs
- Gemma 2 9B — Runs
- Gemma 3 12B — Runs
- Qwen2.5 14B — Runs, 50.3 tok/s median
- DeepSeek-R1-Distill-Qwen 14B — Runs, 48.4 tok/s median
- Qwen3 14B — Runs, 40.7 tok/s median
- Phi-4 14B — Runs, 52.6 tok/s median
- gpt-oss-20b — Runs
- Gemma 3 27B — Runs
- Gemma 2 27B — Runs, 31.6 tok/s median
- Qwen3 30B-A3B — Runs
- Qwen3 32B — Runs, 27.5 tok/s median
- Qwen2.5 32B — Runs, 26.1 tok/s median
- DeepSeek-R1-Distill-Qwen 32B — Runs, 26.2 tok/s median
- Llama 3.1 70B — Runs, 14.6 tok/s median
- Llama 3.3 70B — Runs, 13.6 tok/s median
- DeepSeek-R1-Distill-Llama 70B — Runs, 13.9 tok/s median
How the checks work
The fit is the Q4_K_M file size against the card's VRAM, with 1.5 GB kept for the runtime and an ~8K context. A published run counts only if the pair fits, the run is Q4-class, its source does not call it an estimate, it is under the bandwidth ceiling above, and it is not one figure pasted across many cards. The whole matrix is downloadable as a CSV under CC BY 4.0.
As an Amazon Associate, SpecPicks earns from qualifying purchases.
This page is editorial synthesis based on publicly available information. No independent first-party benchmarking is reported.