Will It Run AI

Can japanese stablelm instruct gamma 7B run on GTX 1080 Ti 11GB?

YES — Runs Great

B55Good
Estimated from fit model

japanese stablelm instruct gamma 7B needs ~7.4 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~67 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
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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) — 7.4 GB, 66.9 tok/s, Runs well
7.4 GB required11.0 GB available
67% VRAM used

Fit status

Runs well

Decode

66.9 tok/s

TTFT

2895 ms

Safe context

86K

Memory

7.4 GB / 11.0 GB

Memory breakdown

Weights4.3 GB
KV Cache0.8 GB
Runtime1.2 GB
Headroom1.1 GB

See how fast it feels

See how fast it feelsjapanese stablelm instruct gamma 7B on GTX 1080 Ti 11GB
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: 66.9 tok/s decode · 2.9s TTFT (warm) · 167 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well66.9 tok/s1579 ms86K
CodingBRuns well66.9 tok/s2895 ms86K
Agentic CodingBRuns well66.9 tok/s4211 ms86K
ReasoningBRuns well66.9 tok/s3421 ms86K
RAGBRuns well66.9 tok/s5263 ms86K

Quantization options

How japanese stablelm instruct gamma 7B (7B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowC50
Q3_K_S
3
3.4 GB
LowC50
NVFP4
4
3.9 GB
MediumC51
Q4_K_M
4
4.3 GB
MediumC52
Q5_K_M
5
5.0 GB
HighC52
Q6_K
6
5.7 GB
HighC52
Q8_0Best for your GPU
8
7.5 GB
Very HighC52
F16
16
14.3 GB
MaximumF0

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

Frequently asked questions

Can GTX 1080 Ti 11GB run japanese stablelm instruct gamma 7B?

Yes, GTX 1080 Ti 11GB can run japanese stablelm instruct gamma 7B with a B grade (Runs well). Expected decode speed: 66.9 tok/s.

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

japanese stablelm instruct gamma 7B (7B parameters) requires approximately 7.4 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 GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, japanese stablelm instruct gamma 7B achieves approximately 66.9 tokens per second decode speed with a time-to-first-token of 2895ms using Q4_K_M quantization.

Can GTX 1080 Ti 11GB run japanese stablelm instruct gamma 7B for coding?

For coding workloads, japanese stablelm instruct gamma 7B on GTX 1080 Ti 11GB receives a B grade with 66.9 tok/s and 86K context.

What context window can japanese stablelm instruct gamma 7B use on GTX 1080 Ti 11GB?

On GTX 1080 Ti 11GB, japanese stablelm instruct gamma 7B can safely use up to 86K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for GTX 1080 Ti 11GBSee all hardware for japanese stablelm instruct gamma 7B
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