Can stabilityai japanese stablelm instruct beta 70b run on RTX PRO 6000 Blackwell Server Edition 96GB?

YES — Runs Great

C52Usable

Estimated from fit model

stabilityai japanese stablelm instruct beta 70b needs ~61.7 GB VRAM. RTX PRO 6000 Blackwell Server Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~31 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 61.7 GB, 31.4 tok/s, Runs well

61.7 GB required96.0 GB available

64% VRAM used

Fit status

Runs well

Decode

31.4 tok/s

TTFT

6162 ms

Safe context

83K

Memory

61.7 GB / 96.0 GB

Memory breakdown

Weights42.7 GB

KV Cache8.2 GB

Runtime1.2 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsstabilityai   japanese stablelm instruct beta 70b on RTX PRO 6000 Blackwell Server Edition 96GB
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: 31.4 tok/s decode · 6.2s TTFT (warm) · 79 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	31.4 tok/s	3361 ms	83K
Coding	C	Runs well	31.4 tok/s	6162 ms	83K
Agentic Coding	C	Runs well	31.4 tok/s	8963 ms	83K
Reasoning	C	Runs well	31.4 tok/s	7283 ms	83K
RAG	C	Runs well	31.4 tok/s	11204 ms	83K

Inference speed

stabilityai japanese stablelm instruct beta 70b inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for stabilityai japanese stablelm instruct beta 70b at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is 2× RX 7900 XTX 24GB at ~15 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?
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	14.6	Heavy offload
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	14.1	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	13.0	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	10.9	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	10.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	9.9	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	7.7	Heavy offload
RTX 5090 32GB	32 GB	Q4_K_M	7.0	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	7.0	Heavy offload
4× RTX 3060 12GB	48 GB	Q4_K_M	6.2	Heavy offload
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	4.7	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	4.0	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	3.6	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.7	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.4	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.3	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.1	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on RTX PRO 6000 Blackwell Server Edition 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	27.3 GB	Low	C42
Q3_K_S	3	34.3 GB	Low	C43
NVFP4	4	39.2 GB	Medium	C44
Q4_K_M	4	42.7 GB	Medium	C45
Q5_K_M	5	50.4 GB	High	C47
Q6_K	6	57.4 GB	High	C47
Q8_0Best for your GPU	8	74.9 GB	Very High	C47
F16	16	143.5 GB	Maximum	F0

Get started

Copy-paste commands to run stabilityai japanese stablelm instruct beta 70b on your machine.

Run

lms load hf-richarderkhov--stabilityai---japanese-stablelm-instruct-beta-70b-gguf && lms server start

アップグレードオプション

stabilityai japanese stablelm instruct beta 70bを快適に動かすハードウェア

AMD Instinct MI300A 128GBコスパ重視

128 GB VRAM (+32)5300 GB/s (+3703)

Raises estimated decode speed by about 177%.86.9 tok/sデコード

Raises estimated decode speed by about 177%.

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

〜$12,000 MSRP

Frequently asked questions

Can RTX PRO 6000 Blackwell Server Edition 96GB run stabilityai japanese stablelm instruct beta 70b?

Yes, RTX PRO 6000 Blackwell Server Edition 96GB can run stabilityai japanese stablelm instruct beta 70b with a C grade (Runs well). Expected decode speed: 31.4 tok/s.

How much VRAM does stabilityai japanese stablelm instruct beta 70b need?

stabilityai japanese stablelm instruct beta 70b (70B parameters) requires approximately 61.7 GB of memory with Q4_K_M quantization.

What is the best quantization for stabilityai japanese stablelm instruct beta 70b?

The recommended quantization for stabilityai japanese stablelm instruct beta 70b is Q4_K_M, which balances quality and memory efficiency.

What speed will stabilityai japanese stablelm instruct beta 70b run at on RTX PRO 6000 Blackwell Server Edition 96GB?

On RTX PRO 6000 Blackwell Server Edition 96GB, stabilityai japanese stablelm instruct beta 70b achieves approximately 31.4 tokens per second decode speed with a time-to-first-token of 6162ms using Q4_K_M quantization.

Can RTX PRO 6000 Blackwell Server Edition 96GB run stabilityai japanese stablelm instruct beta 70b for coding?

For coding workloads, stabilityai japanese stablelm instruct beta 70b on RTX PRO 6000 Blackwell Server Edition 96GB receives a C grade with 31.4 tok/s and 83K context.

What context window can stabilityai japanese stablelm instruct beta 70b use on RTX PRO 6000 Blackwell Server Edition 96GB?

On RTX PRO 6000 Blackwell Server Edition 96GB, stabilityai japanese stablelm instruct beta 70b can safely use up to 83K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

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