Can japanese stablelm instruct gamma 7B run on Radeon Pro W6800 32GB?

YES — Runs Great

C47Usable

Estimated from fit model

japanese stablelm instruct gamma 7B needs ~9.2 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~67 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 9.2 GB, 67.1 tok/s, Runs well

9.2 GB required32.0 GB available

29% VRAM used

Fit status

Runs well

Decode

67.1 tok/s

TTFT

2883 ms

Safe context

461K

Memory

9.2 GB / 32.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsjapanese stablelm instruct gamma 7B on Radeon Pro W6800 32GB
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: 67.1 tok/s decode · 2.9s TTFT (warm) · 168 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	67.1 tok/s	1573 ms	461K
Coding	C	Runs well	67.1 tok/s	2883 ms	461K
Agentic Coding	C	Runs well	67.1 tok/s	4194 ms	461K
Reasoning	C	Runs well	67.1 tok/s	3407 ms	461K
RAG	C	Runs well	67.1 tok/s	5242 ms	461K

Quantization options

How japanese stablelm instruct gamma 7B (7B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C43
Q3_K_S	3	3.4 GB	Low	C43
NVFP4	4

Get started

Copy-paste commands to run japanese stablelm instruct gamma 7B on your machine.

Run

lms load hf-thebloke--japanese-stablelm-instruct-gamma-7b-gguf && lms server start

Upgrade options

Hardware that runs japanese stablelm instruct gamma 7B well

MacBook Pro M4 Max 48GBBudget pick

48 GB Unified (+16)546 GB/s (+34)

Raises estimated decode speed by about 31%.87.8 tok/s decode

Raises estimated decode speed by about 31%.

~$2,499 MSRP

MacBook Pro M3 Max 48GBBest value

48 GB Unified (+16)

This setup is broadly balanced for this model.56.2 tok/s decode

~$2,499 MSRP

Frequently asked questions

Can Radeon Pro W6800 32GB run japanese stablelm instruct gamma 7B?

Yes, Radeon Pro W6800 32GB can run japanese stablelm instruct gamma 7B with a C grade (Runs well). Expected decode speed: 67.1 tok/s.

How much VRAM does japanese stablelm instruct gamma 7B need?

japanese stablelm instruct gamma 7B (7B parameters) requires approximately 9.2 GB of memory with Q4_K_M quantization.

What is the best quantization for japanese stablelm instruct gamma 7B?

The recommended quantization for japanese stablelm instruct gamma 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will japanese stablelm instruct gamma 7B run at on Radeon Pro W6800 32GB?

On Radeon Pro W6800 32GB, japanese stablelm instruct gamma 7B achieves approximately 67.1 tokens per second decode speed with a time-to-first-token of 2883ms using Q4_K_M quantization.

Can Radeon Pro W6800 32GB run japanese stablelm instruct gamma 7B for coding?

For coding workloads, japanese stablelm instruct gamma 7B on Radeon Pro W6800 32GB receives a C grade with 67.1 tok/s and 461K context.

What context window can japanese stablelm instruct gamma 7B use on Radeon Pro W6800 32GB?

On Radeon Pro W6800 32GB, japanese stablelm instruct gamma 7B can safely use up to 461K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon Pro W6800 32GB See all hardware for japanese stablelm instruct gamma 7B

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