Mac Studio M2 Ultra 128GBOpção econômica
128 GB Unified (+48)
C
This setup is broadly balanced for this model.95.1 tok/s decodificação
~$3,999 MSRP
Can internlm3 8b instruct abliterated i1 run on NVIDIA A800 80GB?
YES — Runs Great
C45Usable
Estimated from fit model
internlm3 8b instruct abliterated i1 needs ~15.0 GB VRAM. NVIDIA A800 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 feelsinternlm3 8b instruct abliterated i1 on NVIDIA A800 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 |
Quantization options
How internlm3 8b instruct abliterated i1 (8B params) fits at each quantization level on NVIDIA A800 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 internlm3 8b instruct abliterated i1 on your machine.
Run
lms load hf-mradermacher--internlm3-8b-instruct-abliterated-i1-gguf && lms server startOpções de upgrade
Hardware que roda bem internlm3 8b instruct abliterated i1
Frequently asked questions
Can NVIDIA A800 80GB run internlm3 8b instruct abliterated i1?
Yes, NVIDIA A800 80GB can run internlm3 8b instruct abliterated i1 with a C grade (Runs well). Expected decode speed: 112.0 tok/s.
How much VRAM does internlm3 8b instruct abliterated i1 need?
internlm3 8b instruct abliterated i1 (8B parameters) requires approximately 15.0 GB of memory with Q4_K_M quantization.
What is the best quantization for internlm3 8b instruct abliterated i1?
The recommended quantization for internlm3 8b instruct abliterated i1 is Q4_K_M, which balances quality and memory efficiency.
What speed will internlm3 8b instruct abliterated i1 run at on NVIDIA A800 80GB?
On NVIDIA A800 80GB, internlm3 8b instruct abliterated 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 A800 80GB run internlm3 8b instruct abliterated i1 for coding?
For coding workloads, internlm3 8b instruct abliterated i1 on NVIDIA A800 80GB receives a C grade with 112.0 tok/s and 1.1M context.
What context window can internlm3 8b instruct abliterated i1 use on NVIDIA A800 80GB?
On NVIDIA A800 80GB, internlm3 8b instruct abliterated 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.
Embed this result▼
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-mradermacher--internlm3-8b-instruct-abliterated-i1-gguf-on-a800-80gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: