Mac Studio M2 Ultra 128GBGünstige Wahl
128 GB Unified (+48)
C
This setup is broadly balanced for this model.95.1 tok/s Dekodierung
ca. $3,999 MSRP
Can internlm2 5 1 8b chat i1 run on NVIDIA H100 80GB?
YES — Runs Great
C45Usable
Estimated from fit model
internlm2 5 1 8b chat i1 needs ~15.0 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~112 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.
Select quantization to explore
Q4_K_M (Medium quality) — 15.0 GB, 112.0 tok/s, Runs well
15.0 GB required80.0 GB available
19% VRAM used
Fit status
Runs well
Decode
112.0 tok/s
TTFT
1729 ms
Safe context
1.1M
Memory
15.0 GB / 80.0 GB
Memory breakdown
Weights4.9 GB
KV Cache0.9 GB
Runtime1.2 GB
Headroom8.0 GB
See how fast it feels
See how fast it feelsinternlm2 5 1 8b chat i1 on NVIDIA H100 80GB
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: 112.0 tok/s decode · 1.7s TTFT (warm) · 280 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 | 112.0 tok/s | 943 ms | 1.1M |
| Coding | C | Runs well | 112.0 tok/s | 1729 ms | 1.1M |
| Agentic Coding | C | Runs well | 112.0 tok/s | 2514 ms | 1.1M |
| Reasoning | C | Runs well | 112.0 tok/s | 2043 ms | 1.1M |
| RAG | C | Runs well | 112.0 tok/s | 3143 ms | 1.1M |
Inference speed
internlm2 5 1 8b chat i1 inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for internlm2 5 1 8b chat i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~112 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? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 112.0 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 112.0 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 112.0 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 112.0 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 95.1 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 90.2 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 77.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 76.8 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 76.8 | Fits |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 49.2 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 48.7 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 45.1 | Fits |
| RTX 4060 8GB | 8 GB | Q4_K_M | 40.7 | Offloads |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 39.6 | Fits |
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 internlm2 5 1 8b chat i1 (8B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | D39 |
Q3_K_S | 3 | 3.9 GB | Low | D39 |
NVFP4 | 4 | 4.5 GB | Medium | D39 |
Q4_K_M | 4 | 4.9 GB | Medium | D39 |
Q5_K_M | 5 | 5.8 GB | High | D39 |
Q6_K | 6 | 6.6 GB | High | D39 |
Q8_0 | 8 | 8.6 GB | Very High | D40 |
F16Best for your GPU | 16 | 16.4 GB | Maximum | C41 |
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 startUpgrade-Optionen
Hardware, die internlm2 5 1 8b chat i1 gut ausführt
Frequently asked questions
Can NVIDIA H100 80GB run internlm2 5 1 8b chat i1?
Yes, NVIDIA H100 80GB can run internlm2 5 1 8b chat i1 with a C grade (Runs well). Expected decode speed: 112.0 tok/s.
How much VRAM does internlm2 5 1 8b chat i1 need?
internlm2 5 1 8b chat i1 (8B parameters) requires approximately 15.0 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 H100 80GB?
On NVIDIA H100 80GB, internlm2 5 1 8b chat i1 achieves approximately 112.0 tokens per second decode speed with a time-to-first-token of 1729ms using Q4_K_M quantization.
Can NVIDIA H100 80GB run internlm2 5 1 8b chat i1 for coding?
For coding workloads, internlm2 5 1 8b chat i1 on NVIDIA H100 80GB receives a C grade with 112.0 tok/s and 1.1M context.
What context window can internlm2 5 1 8b chat i1 use on NVIDIA H100 80GB?
On NVIDIA H100 80GB, internlm2 5 1 8b chat i1 can safely use up to 1.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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