Can SOLAR 10.7B Instruct v1.0 uncensored run on Radeon PRO W7900 DS 48GB?

YES — Runs Great

C47Usable

Estimated from fit model

SOLAR 10.7B Instruct v1.0 uncensored needs ~13.5 GB VRAM. Radeon PRO W7900 DS 48GB has 48.0 GB. With Q4_K_M quantization, expect ~78 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

Q4_K_M (Medium quality) — 13.5 GB, 78.1 tok/s, Runs well

13.5 GB required48.0 GB available

28% VRAM used

Fit status

Runs well

Decode

78.1 tok/s

TTFT

2479 ms

Safe context

456K

Memory

13.5 GB / 48.0 GB

Memory breakdown

Weights6.5 GB

KV Cache1.3 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsSOLAR 10.7B Instruct v1.0 uncensored 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: 78.1 tok/s decode · 2.5s TTFT (warm) · 195 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	78.1 tok/s	1352 ms	456K
Coding	C	Runs well	78.1 tok/s	2479 ms	456K
Agentic Coding	C	Runs well	78.1 tok/s	3606 ms	456K
Reasoning	C	Runs well	78.1 tok/s	2930 ms	456K
RAG	C	Runs well	78.1 tok/s	4507 ms	456K

Quantization options

How SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B params) fits at each quantization level on Radeon PRO W7900 DS 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.2 GB	Low	C41
Q3_K_S	3	5.2 GB	Low	C42
NVFP4	4

Get started

Copy-paste commands to run SOLAR 10.7B Instruct v1.0 uncensored on your machine.

Run

lms load hf-thebloke--solar-10-7b-instruct-v1-0-uncensored-gguf && lms server start

Upgrade options

Hardware that runs SOLAR 10.7B Instruct v1.0 uncensored well

Mac Studio M3 Ultra 96GBBudget pick

96 GB Unified (+48)

Adds memory headroom for longer context windows and future model growth.85.3 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$3,999 MSRP

Frequently asked questions

Can Radeon PRO W7900 DS 48GB run SOLAR 10.7B Instruct v1.0 uncensored?

Yes, Radeon PRO W7900 DS 48GB can run SOLAR 10.7B Instruct v1.0 uncensored with a C grade (Runs well). Expected decode speed: 78.1 tok/s.

How much VRAM does SOLAR 10.7B Instruct v1.0 uncensored need?

SOLAR 10.7B Instruct v1.0 uncensored (10.699999809265137B parameters) requires approximately 13.5 GB of memory with Q4_K_M quantization.

What is the best quantization for SOLAR 10.7B Instruct v1.0 uncensored?

The recommended quantization for SOLAR 10.7B Instruct v1.0 uncensored is Q4_K_M, which balances quality and memory efficiency.

What speed will SOLAR 10.7B Instruct v1.0 uncensored run at on Radeon PRO W7900 DS 48GB?

On Radeon PRO W7900 DS 48GB, SOLAR 10.7B Instruct v1.0 uncensored achieves approximately 78.1 tokens per second decode speed with a time-to-first-token of 2479ms using Q4_K_M quantization.

Can Radeon PRO W7900 DS 48GB run SOLAR 10.7B Instruct v1.0 uncensored for coding?

For coding workloads, SOLAR 10.7B Instruct v1.0 uncensored on Radeon PRO W7900 DS 48GB receives a C grade with 78.1 tok/s and 456K context.

What context window can SOLAR 10.7B Instruct v1.0 uncensored use on Radeon PRO W7900 DS 48GB?

On Radeon PRO W7900 DS 48GB, SOLAR 10.7B Instruct v1.0 uncensored can safely use up to 456K 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 48GB See all hardware for SOLAR 10.7B Instruct v1.0 uncensored

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