Can japanese stablelm instruct gamma 7B run on RTX 4060 Laptop 8GB?

YES — Tight Fit

C51Usable

Estimated from fit model

japanese stablelm instruct gamma 7B needs ~7.1 GB VRAM. RTX 4060 Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~45 tok/s.

Runtime: OllamaCapacity: TightBandwidth: LowStack: BasicBottleneck: 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) — 7.1 GB, 45.0 tok/s, Tight fit

7.1 GB required8.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

45.0 tok/s

TTFT

4306 ms

Safe context

34K

Memory

7.1 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsjapanese stablelm instruct gamma 7B on RTX 4060 Laptop 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: 45.0 tok/s decode · 4.3s TTFT (warm) · 112 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	Tight fit	45.0 tok/s	2349 ms	34K
Coding	C	Tight fit	45.0 tok/s	4306 ms	34K
Agentic Coding	C	Runs with offload	45.0 tok/s	6263 ms	34K
Reasoning	C	Tight fit	45.0 tok/s	5088 ms	34K
RAG	C	Runs with offload	45.0 tok/s	7828 ms	34K

Quantization options

How japanese stablelm instruct gamma 7B (7B params) fits at each quantization level on RTX 4060 Laptop 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

RTX 3060 12GBBudget pick

12 GB VRAM (+4)360 GB/s (+104)

Adds memory headroom for longer context windows and future model growth.55.6 tok/s decode

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

~$329 MSRP

RTX 5070 12GBBest value

12 GB VRAM (+4)672 GB/s (+416)

Raises estimated decode speed by about 118%.98 tok/s decode

Raises estimated decode speed by about 118%.

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

~$549 MSRP

RTX 4070 Super 12GBNVIDIA upgrade

12 GB VRAM (+4)504 GB/s (+248)

Raises estimated decode speed by about 102%.90.9 tok/s decode

Raises estimated decode speed by about 102%.

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

~$599 MSRP

Frequently asked questions

Can RTX 4060 Laptop 8GB run japanese stablelm instruct gamma 7B?

Yes, RTX 4060 Laptop 8GB can run japanese stablelm instruct gamma 7B with a C grade (Tight fit). Expected decode speed: 45.0 tok/s.

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

japanese stablelm instruct gamma 7B (7B parameters) requires approximately 7.1 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 RTX 4060 Laptop 8GB?

On RTX 4060 Laptop 8GB, japanese stablelm instruct gamma 7B achieves approximately 45.0 tokens per second decode speed with a time-to-first-token of 4306ms using Q4_K_M quantization.

Can RTX 4060 Laptop 8GB run japanese stablelm instruct gamma 7B for coding?

For coding workloads, japanese stablelm instruct gamma 7B on RTX 4060 Laptop 8GB receives a C grade with 45.0 tok/s and 34K context.

What context window can japanese stablelm instruct gamma 7B use on RTX 4060 Laptop 8GB?

On RTX 4060 Laptop 8GB, japanese stablelm instruct gamma 7B can safely use up to 34K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 4060 Laptop 8GB See all hardware for japanese stablelm instruct gamma 7B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-thebloke--japanese-stablelm-instruct-gamma-7b-gguf-on-rtx-4060-laptop-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: