Will It Run AI

Can stabilityai japanese stablelm instruct beta 70b run on RTX PRO 6000 Blackwell Workstation 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 Workstation Edition 96GB has 96.0 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: HighStack: 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) — 61.7 GB, 35.3 tok/s, Runs well
61.7 GB required96.0 GB available
64% VRAM used

Fit status

Runs well

Decode

35.3 tok/s

TTFT

5492 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 Workstation 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: 35.3 tok/s decode · 5.5s TTFT (warm) · 88 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well35.3 tok/s2996 ms83K
CodingCRuns well35.3 tok/s5492 ms83K
Agentic CodingCRuns well35.3 tok/s7988 ms83K
ReasoningCRuns well35.3 tok/s6490 ms83K
RAGCRuns well35.3 tok/s9985 ms83K

Quantization options

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

QuantBitsVRAMQualityFit
Q2_K
2
27.3 GB
LowC42
Q3_K_S
3
34.3 GB
LowC43
NVFP4
4
39.2 GB
MediumC44
Q4_K_M
4
42.7 GB
MediumC45
Q5_K_M
5
50.4 GB
HighC47
Q6_K
6
57.4 GB
HighC47
Q8_0Best for your GPU
8
74.9 GB
Very HighC47
F16
16
143.5 GB
MaximumF0

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

Upgrade-Optionen

Hardware, die stabilityai japanese stablelm instruct beta 70b gut ausführt

AMD Instinct MI300A 128GBGünstige Wahl
128 GB VRAM (+32)5300 GB/s (+3508)
C
Raises estimated decode speed by about 146%.86.9 tok/s Dekodierung

Raises estimated decode speed by about 146%.

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

ca. $12,000 MSRP

Frequently asked questions

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

Yes, RTX PRO 6000 Blackwell Workstation Edition 96GB can run stabilityai japanese stablelm instruct beta 70b with a C grade (Runs well). Expected decode speed: 35.3 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 Workstation Edition 96GB?

On RTX PRO 6000 Blackwell Workstation Edition 96GB, stabilityai japanese stablelm instruct beta 70b achieves approximately 35.3 tokens per second decode speed with a time-to-first-token of 5492ms using Q4_K_M quantization.

Can RTX PRO 6000 Blackwell Workstation 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 Workstation Edition 96GB receives a C grade with 35.3 tok/s and 83K context.

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

On RTX PRO 6000 Blackwell Workstation 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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