Can japanese stablelm instruct gamma 7B run on Radeon Pro W7500 8GB?

YES — Tight Fit

C50Usable

Estimated from fit model

japanese stablelm instruct gamma 7B needs ~6.8 GB VRAM. Radeon Pro W7500 8GB has 8.0 GB. With Q4_K_M quantization, expect ~31 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 6.8 GB, 31.0 tok/s, Tight fit

6.8 GB required8.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

31.0 tok/s

TTFT

6255 ms

Safe context

40K

Memory

6.8 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsjapanese stablelm instruct gamma 7B on Radeon Pro W7500 8GB
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: 31.0 tok/s decode · 6.3s TTFT (warm) · 77 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	31.0 tok/s	3412 ms	40K
Coding	C	Tight fit	31.0 tok/s	6255 ms	40K
Agentic Coding	C	Runs with offload	31.0 tok/s	9098 ms	40K
Reasoning	C	Tight fit	31.0 tok/s	7392 ms	40K
RAG	C	Runs with offload	31.0 tok/s	11373 ms	40K

Quantization options

How japanese stablelm instruct gamma 7B (7B params) fits at each quantization level on Radeon Pro W7500 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C53
Q3_K_S	3	3.4 GB	Low	C53
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

RX 7700 XT 12GBBudget pick

12 GB VRAM (+4)432 GB/s (+208)

Raises estimated decode speed by about 96%.60.7 tok/s decode

Raises estimated decode speed by about 96%.

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

~$449 MSRP

RX 6700 XT 12GBBest value

12 GB VRAM (+4)384 GB/s (+160)

Raises estimated decode speed by about 51%.46.8 tok/s decode

Raises estimated decode speed by about 51%.

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

~$479 MSRP

RX 9070 16GBAMD upgrade

16 GB VRAM (+8)640 GB/s (+416)

Raises estimated decode speed by about 200%.92.9 tok/s decode

Raises estimated decode speed by about 200%.

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

~$479 MSRP

Frequently asked questions

Can Radeon Pro W7500 8GB run japanese stablelm instruct gamma 7B?

Yes, Radeon Pro W7500 8GB can run japanese stablelm instruct gamma 7B with a C grade (Tight fit). Expected decode speed: 31.0 tok/s.

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

japanese stablelm instruct gamma 7B (7B parameters) requires approximately 6.8 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 W7500 8GB?

On Radeon Pro W7500 8GB, japanese stablelm instruct gamma 7B achieves approximately 31.0 tokens per second decode speed with a time-to-first-token of 6255ms using Q4_K_M quantization.

Can Radeon Pro W7500 8GB run japanese stablelm instruct gamma 7B for coding?

For coding workloads, japanese stablelm instruct gamma 7B on Radeon Pro W7500 8GB receives a C grade with 31.0 tok/s and 40K context.

What context window can japanese stablelm instruct gamma 7B use on Radeon Pro W7500 8GB?

On Radeon Pro W7500 8GB, japanese stablelm instruct gamma 7B can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Radeon Pro W7500 8GB See all hardware for japanese stablelm instruct gamma 7B

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