stabilityai japanese stablelm instruct beta 70b needs ~56.9 GB VRAM. RTX A6000 48GB has 48.0 GB. With Q4_K_M quantization, expect ~7 tok/s.
Operating mode
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.
Select quantization to explore
8.9 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~6.7 GB host RAM)
Decode
7.2 tok/s
TTFT
27018 ms
Safe context
4K
Memory
56.9 GB / 48.0 GB
Offload
20%
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.
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 {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Very compromised (needs ~3.9 GB host RAM) | 8.4 tok/s | 12590 ms | 4K |
| Coding | D | Very compromised | 7.2 tok/s | 27018 ms | 4K |
| Agentic Coding | F | Too heavy | 5.4 tok/s | 52178 ms | 4K |
| Reasoning | D | Very compromised (needs ~6.7 GB host RAM) | 7.2 tok/s | 31930 ms | 4K |
| RAG | F | Too heavy | 5.4 tok/s | 65222 ms | 4K |
How stabilityai japanese stablelm instruct beta 70b (70B params) fits at each quantization level on RTX A6000 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 27.3 GB | Low | C48 |
Q3_K_SBest for your GPU | 3 | 34.3 GB | Low | C47 |
NVFP4 | 4 | 39.2 GB | Medium | F0 |
Q4_K_M | 4 | 42.7 GB | Medium | F0 |
Q5_K_M | 5 | 50.4 GB | High | F0 |
Q6_K | 6 | 57.4 GB | High | F0 |
Q8_0 | 8 | 74.9 GB | Very High | F0 |
F16 | 16 | 143.5 GB | Maximum | F0 |
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
NVIDIA A16 64GBコスパ重視Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 53%.
〜$6,500 MSRP
RTX PRO 6000 Blackwell Workstation Edition 96GBコスパ最良Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 390%.
〜$9,999 MSRP
RTX PRO 6000 Blackwell Server Edition 96GBNVIDIAアップグレードRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 336%.
〜$9,999 MSRP
Yes, RTX A6000 48GB can run stabilityai japanese stablelm instruct beta 70b with a D grade (Very compromised). Expected decode speed: 7.2 tok/s.
stabilityai japanese stablelm instruct beta 70b (70B parameters) requires approximately 56.9 GB of memory with Q4_K_M quantization.
The recommended quantization for stabilityai japanese stablelm instruct beta 70b is Q4_K_M, which balances quality and memory efficiency.
On RTX A6000 48GB, stabilityai japanese stablelm instruct beta 70b achieves approximately 7.2 tokens per second decode speed with a time-to-first-token of 27018ms using Q4_K_M quantization.
For coding workloads, stabilityai japanese stablelm instruct beta 70b on RTX A6000 48GB receives a D grade with 7.2 tok/s and 4K context.
On RTX A6000 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.
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.
Paste this snippet into any page to show a live fit card.
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