RichardErkhov

stabilityai japanese stablelm base gamma 7b

Name: stabilityai japanese stablelm base gamma 7b
Rating: 49 (193 reviews)
Author: RichardErkhov

Limited data available — some specs may be incomplete or estimated.

stabilityai japanese stablelm base gamma 7b (7B parameters) requires approximately 6.6 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 8 GB of VRAM.

Quick specs

Parameters7B

Architecturedense

Context0K tokens

Modalitytext

Min RAM2.7 GB

Rec. RAM4.3 GB (Q4_K_M)

LicenseUnknown

FamilyUnknown

✓ Chat

Related models

Inference speed

stabilityai japanese stablelm base gamma 7b inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for stabilityai japanese stablelm base gamma 7b at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~98 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	98.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	98.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	98.0

Quick picks

Best budgetC

Intel Arc A580 8GB~$179 — 59 tok/s

Best overallB

RTX 3080 10GB~$699 — 98 tok/s

Best hardware

Top picks for stabilityai japanese stablelm base gamma 7b

Run this model

stabilityai japanese stablelm base gamma 7b on RTX 3080 10GB stabilityai japanese stablelm base gamma 7b on RTX 2080 Ti 11GB stabilityai japanese stablelm base gamma 7b on GTX 1080 Ti 11GB

Quantization

stabilityai japanese stablelm base gamma 7b quantization — VRAM & quality by quant level

How much VRAM stabilityai japanese stablelm base gamma 7b (7B) needs at each GGUF quant, and whether it fits a 24 GB card (RTX 4090 / 3090). The recommended Q4_K_M uses ~4.3 GB — about 43% less VRAM than Q8_0, at a small quality cost.

Quant	Bits	VRAM (weights)	Quality	Fits 24 GB?
Q2_K	2	2.7 GB	Low	Fits
Q3_K_S	3	3.4 GB	Low	Fits
NVFP4	4	3.9 GB	Medium	Fits
Q4_K_Mrecommended	4	4.3 GB	Medium	Fits

Hardware compatibility

Fit estimates across all hardware

Open calculator

Computing compatibility...

Memory breakdown

Reference: RTX 2060 6GB

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom0.6 GB

Frequently asked questions

FAQ — stabilityai japanese stablelm base gamma 7b

How much VRAM does stabilityai japanese stablelm base gamma 7b need?

stabilityai japanese stablelm base gamma 7b (7B parameters) requires approximately 6.6 GB of VRAM with Q4_K_M quantization. Lower quantizations like Q4_K_M use less memory but may reduce quality.

Can I run stabilityai japanese stablelm base gamma 7b on a Intel Arc A580 8GB?

Yes, Intel Arc A580 8GB can run stabilityai japanese stablelm base gamma 7b with a compatibility score of 52/100. It provides 8 GB of memory and achieves approximately 58.8 tokens per second.

What is the best quantization for stabilityai japanese stablelm base gamma 7b?

The recommended quantization for stabilityai japanese stablelm base gamma 7b is Q4_K_M, which offers the best balance between model quality and memory efficiency. Higher quantizations preserve more quality but require more VRAM.

What hardware is recommended for stabilityai japanese stablelm base gamma 7b?

The top recommended hardware for stabilityai japanese stablelm base gamma 7b: RTX 3080 10GB (score: 56/100), RTX 2080 Ti 11GB (score: 56/100), GTX 1080 Ti 11GB (score: 55/100). These provide the best combination of memory, bandwidth, and compute for running this model locally.

Is stabilityai japanese stablelm base gamma 7b good for chat?

Yes, stabilityai japanese stablelm base gamma 7b is well-suited for chat. It was designed with these use cases in mind.