Will It Run AI

Can stabilityai japanese stablelm instruct beta 70b run on RTX 6000 Ada 48GB?

BARELY — Tight on Memory

D30Poor
Estimated from fit model

stabilityai japanese stablelm instruct beta 70b needs ~56.6 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q4_K_M quantization, expect ~10 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload
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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) — 56.6 GB, 9.8 tok/s, Very compromised (needs ~6.5 GB host RAM)
56.6 GB required48.0 GB available
118% VRAM needed

8.6 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~6.5 GB host RAM)

Decode

9.8 tok/s

TTFT

19811 ms

Safe context

4K

Memory

56.6 GB / 48.0 GB

Offload

20%

Memory breakdown

Weights42.7 GB
KV Cache8.2 GB
Runtime0.9 GB
Headroom4.8 GB

See how fast it feels

See how fast it feelsstabilityai japanese stablelm instruct beta 70b on RTX 6000 Ada 48GB
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: 9.8 tok/s decode · 19.8s TTFT (warm) · 24 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 20% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 6.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatDVery compromised (needs ~3.7 GB host RAM)11.4 tok/s9223 ms4K
CodingDVery compromised (needs ~6.5 GB host RAM)9.8 tok/s19811 ms4K
Agentic CodingFToo heavy7.3 tok/s38314 ms4K
ReasoningDVery compromised (needs ~6.5 GB host RAM)9.8 tok/s23413 ms4K
RAGFToo heavy7.3 tok/s47892 ms4K

Quantization options

How stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on RTX 6000 Ada 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
27.3 GB
LowC48
Q3_K_SBest for your GPU
3
34.3 GB
LowC47
NVFP4
4
39.2 GB
MediumF0
Q4_K_M
4
42.7 GB
MediumF0
Q5_K_M
5
50.4 GB
HighF0
Q6_K
6
57.4 GB
HighF0
Q8_0
8
74.9 GB
Very HighF0
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

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

NVIDIANVIDIA A16 64GBコスパ重視
64 GB VRAM (+16)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.11 tok/sデコード

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

〜$6,500 MSRP

NVIDIARTX PRO 6000 Blackwell Workstation Edition 96GBコスパ最良
96 GB VRAM (+48)1792 GB/s (+832)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.35.3 tok/sデコード

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 260%.

〜$9,999 MSRP

NVIDIARTX PRO 6000 Blackwell Server Edition 96GBNVIDIAアップグレード
96 GB VRAM (+48)1597 GB/s (+637)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.31.4 tok/sデコード

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 220%.

〜$9,999 MSRP

Frequently asked questions

Can RTX 6000 Ada 48GB run stabilityai japanese stablelm instruct beta 70b?

Yes, RTX 6000 Ada 48GB can run stabilityai japanese stablelm instruct beta 70b with a D grade (Very compromised (needs ~6.5 GB host RAM)). Expected decode speed: 9.8 tok/s.

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

stabilityai japanese stablelm instruct beta 70b (70B parameters) requires approximately 56.6 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 6000 Ada 48GB?

On RTX 6000 Ada 48GB, stabilityai japanese stablelm instruct beta 70b achieves approximately 9.8 tokens per second decode speed with a time-to-first-token of 19811ms using Q4_K_M quantization.

Can RTX 6000 Ada 48GB run stabilityai japanese stablelm instruct beta 70b for coding?

For coding workloads, stabilityai japanese stablelm instruct beta 70b on RTX 6000 Ada 48GB receives a D grade with 9.8 tok/s and 4K context.

What context window can stabilityai japanese stablelm instruct beta 70b use on RTX 6000 Ada 48GB?

On RTX 6000 Ada 48GB, stabilityai japanese stablelm instruct beta 70b can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if stabilityai japanese stablelm instruct beta 70b feels slow on RTX 6000 Ada 48GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RTX 6000 Ada 48GBSee all hardware for stabilityai japanese stablelm instruct beta 70b
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