Will It Run AI

Can Hermes 2 Pro Llama 3 8B run on Radeon PRO W7900 DS 48GB?

YES — Runs Great

C47Usable
Estimated from fit model

Hermes 2 Pro Llama 3 8B needs ~11.5 GB VRAM. Radeon PRO W7900 DS 48GB has 48.0 GB. With Q4_K_M quantization, expect ~105 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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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) — 11.5 GB, 104.5 tok/s, Runs well
11.5 GB required48.0 GB available
24% VRAM used

Fit status

Runs well

Decode

104.5 tok/s

TTFT

1853 ms

Safe context

639K

Memory

11.5 GB / 48.0 GB

Memory breakdown

Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom4.8 GB

See how fast it feels

See how fast it feelsHermes 2 Pro Llama 3 8B on Radeon PRO W7900 DS 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: 104.5 tok/s decode · 1.9s TTFT (warm) · 261 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well104.5 tok/s1011 ms639K
CodingCRuns well104.5 tok/s1853 ms639K
Agentic CodingCRuns well104.5 tok/s2696 ms639K
ReasoningCRuns well104.5 tok/s2190 ms639K
RAGCRuns well104.5 tok/s3370 ms639K

Quantization options

How Hermes 2 Pro Llama 3 8B (8B params) fits at each quantization level on Radeon PRO W7900 DS 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC41
Q3_K_S
3
3.9 GB
LowC41
NVFP4
4
4.5 GB
MediumC42
Q4_K_M
4
4.9 GB
MediumC42
Q5_K_M
5
5.8 GB
HighC42
Q6_K
6
6.6 GB
HighC42
Q8_0
8
8.6 GB
Very HighC42
F16Best for your GPU
16
16.4 GB
MaximumC45

Get started

Copy-paste commands to run Hermes 2 Pro Llama 3 8B on your machine.

Run

lms load hf-nousresearch--hermes-2-pro-llama-3-8b-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien Hermes 2 Pro Llama 3 8B

Mac Studio M3 Ultra 96GBOpción económica
96 GB Unified (+48)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.112 tok/s decodificación

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

~$3,999 MSRP

Frequently asked questions

Can Radeon PRO W7900 DS 48GB run Hermes 2 Pro Llama 3 8B?

Yes, Radeon PRO W7900 DS 48GB can run Hermes 2 Pro Llama 3 8B with a C grade (Runs well). Expected decode speed: 104.5 tok/s.

How much VRAM does Hermes 2 Pro Llama 3 8B need?

Hermes 2 Pro Llama 3 8B (8B parameters) requires approximately 11.5 GB of memory with Q4_K_M quantization.

What is the best quantization for Hermes 2 Pro Llama 3 8B?

The recommended quantization for Hermes 2 Pro Llama 3 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Hermes 2 Pro Llama 3 8B run at on Radeon PRO W7900 DS 48GB?

On Radeon PRO W7900 DS 48GB, Hermes 2 Pro Llama 3 8B achieves approximately 104.5 tokens per second decode speed with a time-to-first-token of 1853ms using Q4_K_M quantization.

Can Radeon PRO W7900 DS 48GB run Hermes 2 Pro Llama 3 8B for coding?

For coding workloads, Hermes 2 Pro Llama 3 8B on Radeon PRO W7900 DS 48GB receives a C grade with 104.5 tok/s and 639K context.

What context window can Hermes 2 Pro Llama 3 8B use on Radeon PRO W7900 DS 48GB?

On Radeon PRO W7900 DS 48GB, Hermes 2 Pro Llama 3 8B can safely use up to 639K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon PRO W7900 DS 48GBSee all hardware for Hermes 2 Pro Llama 3 8B
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