Can Llama 3.2 3B Instruct run on Radeon Pro W7900 48GB?

YES — Runs Great

C43Usable

Estimated from fit model

Llama 3.2 3B Instruct needs ~8.2 GB VRAM. Radeon Pro W7900 48GB has 48.0 GB. With Q5_K_M quantization, expect ~42 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced

Operating mode

Choose the run profile you care about

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.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 8.2 GB, 42.0 tok/s, Runs well

8.2 GB required48.0 GB available

17% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

1.8M

Memory

8.2 GB / 48.0 GB

Memory breakdown

Weights2.2 GB

KV Cache0.4 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsLlama 3.2 3B Instruct on Radeon Pro W7900 48GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	42.0 tok/s	2514 ms	1.8M
Coding	C	Runs well	42.0 tok/s	4610 ms	1.8M
Agentic Coding	C	Runs well	42.0 tok/s	6705 ms	1.8M
Reasoning	C	Runs well	42.0 tok/s	5448 ms	1.8M
RAG	C	Runs well	42.0 tok/s	8381 ms	1.8M

Quantization options

How Llama 3.2 3B Instruct (3B params) fits at each quantization level on Radeon Pro W7900 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	C42
Q3_K_S	3	1.5 GB	Low	C42
NVFP4	4	1.7 GB	Medium	C42
Q4_K_M	4	1.8 GB	Medium	C42
Q5_K_M	5	2.2 GB	High	C42
Q6_K	6	2.5 GB	High	C42
Q8_0	8	3.2 GB	Very High	C42
F16Best for your GPU	16	6.1 GB	Maximum	C42

Get started

Copy-paste commands to run Llama 3.2 3B Instruct on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "bartowski/Llama-3.2-3B-Instruct-GGUF" \
  --hf-file "Llama-3.2-3B-Instruct-GGUF-Q5_K_M.gguf" \
  -c 4096 -ngl 99

Opciones de mejora

Hardware que ejecuta bien Llama 3.2 3B Instruct

MacBook Pro M4 Max 96GBOpción económica

96 GB Unified (+48)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.42 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

MacBook Pro M3 Max 128GBMejor relación calidad-precio

128 GB Unified (+80)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.42 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,499 MSRP

Frequently asked questions

Can Radeon Pro W7900 48GB run Llama 3.2 3B Instruct?

Yes, Radeon Pro W7900 48GB can run Llama 3.2 3B Instruct with a C grade (Runs well). Expected decode speed: 42.0 tok/s.

How much VRAM does Llama 3.2 3B Instruct need?

Llama 3.2 3B Instruct (3B parameters) requires approximately 8.2 GB of memory with Q5_K_M quantization.

What is the best quantization for Llama 3.2 3B Instruct?

The recommended quantization for Llama 3.2 3B Instruct is Q5_K_M, which balances quality and memory efficiency.

What speed will Llama 3.2 3B Instruct run at on Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, Llama 3.2 3B Instruct achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q5_K_M quantization.

Can Radeon Pro W7900 48GB run Llama 3.2 3B Instruct for coding?

For coding workloads, Llama 3.2 3B Instruct on Radeon Pro W7900 48GB receives a C grade with 42.0 tok/s and 1.8M context.

What context window can Llama 3.2 3B Instruct use on Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, Llama 3.2 3B Instruct can safely use up to 1.8M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon Pro W7900 48GB See all hardware for Llama 3.2 3B Instruct

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