Can Llama 3.3 70B Instruct run on Radeon Pro W7900 48GB?

BARELY — Tight on Memory

D30Poor

Estimated from fit model

Llama 3.3 70B Instruct needs ~56.6 GB VRAM. Radeon Pro W7900 48GB has 48.0 GB. With Q4_K_M quantization, expect ~6 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload

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

Q4_K_M (Medium quality) — 56.6 GB, 6.3 tok/s, Very compromised (needs ~6.5 GB host RAM)

56.6 GB required48.0 GB available

118% VRAM needed

8.6 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~6.5 GB host RAM)

Decode

6.3 tok/s

TTFT

30593 ms

Safe context

Memory

56.6 GB / 48.0 GB

Offload

20%

Memory breakdown

Weights42.7 GB

KV Cache8.2 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsLlama 3.3 70B 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: 6.3 tok/s decode · 30.6s TTFT (warm) · 16 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 6.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	D	Very compromised (needs ~3.7 GB host RAM)	7.4 tok/s	14243 ms	4K
Coding	D	Very compromised (needs ~6.5 GB host RAM)	6.3 tok/s	30593 ms	4K
Agentic Coding	F	Too heavy	4.8 tok/s	59166 ms	4K
Reasoning	D	Very compromised (needs ~6.5 GB host RAM)	6.3 tok/s	36155 ms	4K
RAG	F	Too heavy	4.8 tok/s	73958 ms	4K

Inference speed

Llama 3.3 70B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Llama 3.3 70B Instruct at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~15 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	14.6	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	14.1	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	13.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	10.9	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	10.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	9.9	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	7.7	Heavy offload
RTX 5090 32GB	32 GB	Q4_K_M	7.0	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	7.0	Heavy offload
4× RTX 3060 12GB	48 GB	Q4_K_M	6.2	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	4.7	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	4.0	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	3.6	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.7	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.4	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.3	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.1	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Llama 3.3 70B Instruct (70B params) fits at each quantization level on Radeon Pro W7900 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	C48
Q3_K_SBest for your GPU	3	34.3 GB	Low	C48
NVFP4	4	39.2 GB	Medium	F0
Q4_K_M	4	42.7 GB	Medium	F0
Q5_K_M	5	50.4 GB	High	F0
Q6_K	6	57.4 GB	High	F0
Q8_0	8	74.9 GB	Very High	F0
F16	16	143.5 GB	Maximum	F0

Get started

Copy-paste commands to run Llama 3.3 70B Instruct on your machine.

Run

lms load hf-maziyarpanahi--llama-3-3-70b-instruct-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien Llama 3.3 70B Instruct

AMD Instinct MI350X 288GBOpción económica

288 GB VRAM (+240)8000 GB/s (+7136)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.136.8 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 2071%.

~$8,000 MSRP

AMD Instinct MI210 64GBMejor relación calidad-precio

64 GB VRAM (+16)1638 GB/s (+774)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.26.1 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 314%.

~$10,000 MSRP

AMD Instinct MI300A 128GBMejora AMD

128 GB VRAM (+80)5300 GB/s (+4436)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.86.9 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 1279%.

~$12,000 MSRP

Frequently asked questions

Can Radeon Pro W7900 48GB run Llama 3.3 70B Instruct?

Yes, Radeon Pro W7900 48GB can run Llama 3.3 70B Instruct with a D grade (Very compromised (needs ~6.5 GB host RAM)). Expected decode speed: 6.3 tok/s.

How much VRAM does Llama 3.3 70B Instruct need?

Llama 3.3 70B Instruct (70B parameters) requires approximately 56.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.3 70B Instruct?

The recommended quantization for Llama 3.3 70B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Llama 3.3 70B Instruct run at on Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, Llama 3.3 70B Instruct achieves approximately 6.3 tokens per second decode speed with a time-to-first-token of 30593ms using Q4_K_M quantization.

Can Radeon Pro W7900 48GB run Llama 3.3 70B Instruct for coding?

For coding workloads, Llama 3.3 70B Instruct on Radeon Pro W7900 48GB receives a D grade with 6.3 tok/s and 4K context.

What context window can Llama 3.3 70B Instruct use on Radeon Pro W7900 48GB?

On Radeon Pro W7900 48GB, Llama 3.3 70B Instruct can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Llama 3.3 70B Instruct feels slow on Radeon Pro W7900 48GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for Radeon Pro W7900 48GB See all hardware for Llama 3.3 70B Instruct

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