Can japanese stablelm instruct gamma 7B run on RX 5600 XT 6GB?

Q: What should I upgrade first if japanese stablelm instruct gamma 7B feels slow on RX 5600 XT 6GB?

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.

BARELY — Tight on Memory

D39Poor

Estimated from fit model

japanese stablelm instruct gamma 7B needs ~6.6 GB VRAM. RX 5600 XT 6GB has 6.0 GB. With Q4_K_M quantization, expect ~22 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: 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) — 6.6 GB, 21.6 tok/s, Very compromised (needs ~0.4 GB host RAM)

6.6 GB required6.0 GB available

110% VRAM needed

0.6 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.4 GB host RAM)

Decode

21.6 tok/s

TTFT

8967 ms

Safe context

Memory

6.6 GB / 6.0 GB

Offload

10%

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsjapanese stablelm instruct gamma 7B on RX 5600 XT 6GB
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: 21.6 tok/s decode · 9.0s TTFT (warm) · 54 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 10% 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 {ram} 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	C	Runs with offload (needs ~0.1 GB host RAM)	24.7 tok/s	4272 ms	4K
Coding	D	Very compromised	21.6 tok/s	8967 ms	4K
Agentic Coding	F	Too heavy	16.9 tok/s	16697 ms	4K
Reasoning	D	Very compromised (needs ~0.4 GB host RAM)	21.6 tok/s	10598 ms	4K
RAG	F	Too heavy	16.9 tok/s	20871 ms

Quantization options

How japanese stablelm instruct gamma 7B (7B params) fits at each quantization level on RX 5600 XT 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C54
Q3_K_SBest for your GPU	3	3.4 GB	Low	C53

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 580 8GBBudget pick

8 GB VRAM (+2)

Removes host-memory offload, which is usually the single biggest latency and throughput win.25.8 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

~$229 MSRP

RX 9060 8GBBest value

8 GB VRAM (+2)

Removes host-memory offload, which is usually the single biggest latency and throughput win.42.5 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 97%.

~$249 MSRP

RX 7600 8GBAMD upgrade

8 GB VRAM (+2)

Removes host-memory offload, which is usually the single biggest latency and throughput win.39.1 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 81%.

~$269 MSRP

RTX 3080 10GBBiggest leap

10 GB VRAM (+4)760 GB/s (+472)

Removes host-memory offload, which is usually the single biggest latency and throughput win.84 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 289%.

~$699 MSRP

Frequently asked questions

Can RX 5600 XT 6GB run japanese stablelm instruct gamma 7B?

Yes, RX 5600 XT 6GB can run japanese stablelm instruct gamma 7B with a D grade (Very compromised). Expected decode speed: 21.6 tok/s.

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

japanese stablelm instruct gamma 7B (7B parameters) requires approximately 6.6 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 RX 5600 XT 6GB?

On RX 5600 XT 6GB, japanese stablelm instruct gamma 7B achieves approximately 21.6 tokens per second decode speed with a time-to-first-token of 8967ms using Q4_K_M quantization.

Can RX 5600 XT 6GB run japanese stablelm instruct gamma 7B for coding?

For coding workloads, japanese stablelm instruct gamma 7B on RX 5600 XT 6GB receives a D grade with 21.6 tok/s and 4K context.

What context window can japanese stablelm instruct gamma 7B use on RX 5600 XT 6GB?

On RX 5600 XT 6GB, japanese stablelm instruct gamma 7B can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 5600 XT 6GB See all hardware for japanese stablelm instruct gamma 7B

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