Will It Run AI

Can internlm3 8b instruct abliterated i1 run on NVIDIA A40 48GB?

YES — Runs Great

C47Usable
Estimated from fit model

internlm3 8b instruct abliterated 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
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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) — 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 feelsinternlm3 8b instruct abliterated 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well111.2 tok/s949 ms634K
CodingCRuns well111.2 tok/s1740 ms634K
Agentic CodingCRuns well111.2 tok/s2531 ms634K
ReasoningCRuns well111.2 tok/s2057 ms634K
RAGCRuns well111.2 tok/s3164 ms634K

Quantization options

How internlm3 8b instruct abliterated i1 (8B params) fits at each quantization level on NVIDIA A40 48GB (48.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.1 GB
LowC41
Q3_K_S
3
3.9 GB
LowC41
NVFP4
4
4.5 GB
MediumC41
Q4_K_M
4
4.9 GB
MediumC41
Q5_K_M
5
5.8 GB
HighC41
Q6_K
6
6.6 GB
HighC41
Q8_0
8
8.6 GB
Very HighC42
F16Best for your GPU
16
16.4 GB
MaximumC44

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 start

Opções de upgrade

Hardware que roda bem internlm3 8b instruct abliterated i1

Mac Studio M3 Ultra 96GBOpção econômica
96 GB Unified (+48)819 GB/s (+123)
C
Adds memory headroom for longer context windows and future model growth.112 tok/s decodificação

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

~$3,999 MSRP

Frequently asked questions

Can NVIDIA A40 48GB run internlm3 8b instruct abliterated i1?

Yes, NVIDIA A40 48GB can run internlm3 8b instruct abliterated i1 with a C grade (Runs well). Expected decode speed: 111.2 tok/s.

How much VRAM does internlm3 8b instruct abliterated i1 need?

internlm3 8b instruct abliterated i1 (8B parameters) requires approximately 11.8 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 A40 48GB?

On NVIDIA A40 48GB, internlm3 8b instruct abliterated 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 internlm3 8b instruct abliterated i1 for coding?

For coding workloads, internlm3 8b instruct abliterated i1 on NVIDIA A40 48GB receives a C grade with 111.2 tok/s and 634K context.

What context window can internlm3 8b instruct abliterated i1 use on NVIDIA A40 48GB?

On NVIDIA A40 48GB, internlm3 8b instruct abliterated i1 can safely use up to 634K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA A40 48GBSee all hardware for internlm3 8b instruct abliterated i1
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