Skip to content
Run LLM Locally

Llama 3.2 1B Instruct on the Radeon PRO W7900

With 48 GB of VRAM, the Radeon PRO W7900 runs Llama 3.2 1B Instruct at Q4_K_M entirely on the GPU with an 8K context (about 2.1 GB needed). At Q8_0 it fits entirely.

ShareFacebookXWhatsAppLinkedInEmail

Llama 3.2 1B Instruct

Q4_K_M · 8K context

Excellent

The model needs about 2.1 GB and fits in the 46.6 GB available, with 44.5 GB to spare.

Your hardware
Radeon PRO W7900 48 GB · 64 GB RAM
Estimated memory
~2.1 GB estimate
VRAM (usable memory)
46.6 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
319–557 tok/s estimate
Largest context in fast memory
128K tokens
Confidence: low

Based on: memory bandwidth (864 GB/s) and model size

Memory breakdown

~2.1 GB (1.8 GB–2.4 GB) needed · estimate

Fits entirely in VRAM · 44.5 GB left

  • Model weights0.8 GB
  • KV cache0.3 GB
  • Runtime overhead0.8 GB
  • Safety margin0.3 GB
Assumptions: context 8K tokens · KV cache f16 · llama.cpp (ROCm) · flash attention on · weights = real file size

EstimateData updated 24 days agoSource: Run LLM Locally engine, Hugging Face config.json + model card

What makes this estimate less reliable

  • no benchmark for this hardware and model yet

Other quantizations on the Radeon PRO W7900

Llama 3.2 1B Instruct on Radeon PRO W7900 + 64 GB RAM · 8K context
Llama 3.2 1B Instruct Q8_08K~2.6 GB · 251–413 tok/s estimateExcellent
Llama 3.2 1B Instruct Q6_K8K~2.3 GB · 287–486 tok/s estimateExcellent
Llama 3.2 1B Instruct Q5_K_M8K~2.2 GB · 302–521 tok/s estimateExcellent
Llama 3.2 1B Instruct Q4_K_M8K~2.1 GB · 319–557 tok/s estimateExcellent
Llama 3.2 1B Instruct IQ4_XS8K~2 GB · 330–583 tok/s estimateExcellent
Llama 3.2 1B Instruct Q3_K_M8K~2 GB · 339–606 tok/s estimateExcellent

Alternatives

GPUs that run Llama 3.2 1B Instruct Q4_K_M entirely in VRAM

Other models that fit the Radeon PRO W7900

Where to buy a Radeon PRO W7900

Prices and links follow the market you choose.

Radeon PRO W7900

48 GB GDDR6 ECC · 864 GB/s · 295 W

ExcellentRuns Llama 3.2 1B Instruct Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

No current offer imported for this market: the link opens a marketplace search.

Frequently asked questions

Can the Radeon PRO W7900 run Llama 3.2 1B Instruct?
The model needs about 2.1 GB and fits in the 46.6 GB available, with 44.5 GB to spare.
Which quantization should I use on the Radeon PRO W7900?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~2.6 GB / 46.6 GB.
What is the largest context on the Radeon PRO W7900?
At Q4_K_M, about 128K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Llama 3.2 1B Instruct (Radeon PRO W7900)?
We estimate 319–557 tok/s (estimate, confidence low). Based on: memory bandwidth (864 GB/s) and model size.