Mac Studio M3 Ultra 96GBコスパ重視
96 GB Unified (+48)819 GB/s (+123)
C
Adds memory headroom for longer context windows and future model growth.112 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$3,999 MSRP
Can internlm2 5 1 8b chat i1 run on NVIDIA A40 48GB?
YES — Runs Great
C47Usable
Estimated from fit model
internlm2 5 1 8b chat i1 needs ~11.8 GB VRAM. NVIDIA A40 48GB has 48.0 GB. With Q4_K_M quantization, expect ~111 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 11.8 GB, 111.2 tok/s, Runs well
11.8 GB required48.0 GB available
25% VRAM used
Fit status
Runs well
Decode
111.2 tok/s
TTFT
1740 ms
Safe context
634K
Memory
11.8 GB / 48.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom4.8 GB
See how fast it feels
See how fast it feelsinternlm2 5 1 8b chat i1 on NVIDIA A40 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: 111.2 tok/s decode · 1.7s TTFT (warm) · 278 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 | 111.2 tok/s | 949 ms | 634K |
| Coding | C | Runs well | 111.2 tok/s | 1740 ms | 634K |
| Agentic Coding | C | Runs well | 111.2 tok/s | 2531 ms | 634K |
| Reasoning | C | Runs well | 111.2 tok/s | 2057 ms | 634K |
| RAG | C | Runs well | 111.2 tok/s | 3164 ms | 634K |
Quantization options
How internlm2 5 1 8b chat i1 (8B params) fits at each quantization level on NVIDIA A40 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | C41 |
Q3_K_S | 3 | 3.9 GB | Low | C41 |
NVFP4 | 4 | 4.5 GB | Medium | C41 |
Q4_K_M | 4 | 4.9 GB | Medium | C41 |
Q5_K_M | 5 | 5.8 GB | High | C41 |
Q6_K | 6 | 6.6 GB | High | C42 |
Q8_0 | 8 | 8.6 GB | Very High | C42 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C44 |
Get started
Copy-paste commands to run internlm2 5 1 8b chat i1 on your machine.
Run
lms load hf-mradermacher--internlm2-5-1-8b-chat-i1-gguf && lms server startアップグレードオプション
internlm2 5 1 8b chat i1を快適に動かすハードウェア
Frequently asked questions
Can NVIDIA A40 48GB run internlm2 5 1 8b chat i1?
Yes, NVIDIA A40 48GB can run internlm2 5 1 8b chat i1 with a C grade (Runs well). Expected decode speed: 111.2 tok/s.
How much VRAM does internlm2 5 1 8b chat i1 need?
internlm2 5 1 8b chat i1 (8B parameters) requires approximately 11.8 GB of memory with Q4_K_M quantization.
What is the best quantization for internlm2 5 1 8b chat i1?
The recommended quantization for internlm2 5 1 8b chat i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will internlm2 5 1 8b chat i1 run at on NVIDIA A40 48GB?
On NVIDIA A40 48GB, internlm2 5 1 8b chat i1 achieves approximately 111.2 tokens per second decode speed with a time-to-first-token of 1740ms using Q4_K_M quantization.
Can NVIDIA A40 48GB run internlm2 5 1 8b chat i1 for coding?
For coding workloads, internlm2 5 1 8b chat i1 on NVIDIA A40 48GB receives a C grade with 111.2 tok/s and 634K context.
What context window can internlm2 5 1 8b chat i1 use on NVIDIA A40 48GB?
On NVIDIA A40 48GB, internlm2 5 1 8b chat i1 can safely use up to 634K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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