Will It Run AI

Can internlm3 8b instruct abliterated i1 run on Mac Studio M2 Ultra 64GB?

YES — Runs Great

C48Usable
Estimated from fit model

internlm3 8b instruct abliterated i1 needs ~13.6 GB VRAM. Mac Studio M2 Ultra 64GB has 46.1 GB. With Q4_K_M quantization, expect ~95 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 13.6 GB, 95.1 tok/s, Runs well
13.6 GB required46.1 GB available
30% VRAM used

Fit status

Runs well

Decode

95.1 tok/s

TTFT

2036 ms

Safe context

570K

Memory

13.6 GB / 46.1 GB

Memory breakdown

Weights4.9 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom6.9 GB

See how fast it feels

See how fast it feelsinternlm3 8b instruct abliterated i1 on Mac Studio M2 Ultra 64GB
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: 95.1 tok/s decode · 2.0s TTFT (warm) · 238 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well95.1 tok/s1111 ms570K
CodingCRuns well95.1 tok/s2036 ms570K
Agentic CodingCRuns well95.1 tok/s2962 ms570K
ReasoningCRuns well95.1 tok/s2406 ms570K
RAGCRuns well95.1 tok/s3702 ms570K

Quantization options

How internlm3 8b instruct abliterated i1 (8B params) fits at each quantization level on Mac Studio M2 Ultra 64GB (46.1 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
HighC42
Q6_K
6
6.6 GB
HighC42
Q8_0
8
8.6 GB
Very HighC42
F16Best for your GPU
16
16.4 GB
MaximumC45

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

Frequently asked questions

Can Mac Studio M2 Ultra 64GB run internlm3 8b instruct abliterated i1?

Yes, Mac Studio M2 Ultra 64GB can run internlm3 8b instruct abliterated i1 with a C grade (Runs well). Expected decode speed: 95.1 tok/s.

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

internlm3 8b instruct abliterated i1 (8B parameters) requires approximately 13.6 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 Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, internlm3 8b instruct abliterated i1 achieves approximately 95.1 tokens per second decode speed with a time-to-first-token of 2036ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 64GB run internlm3 8b instruct abliterated i1 for coding?

For coding workloads, internlm3 8b instruct abliterated i1 on Mac Studio M2 Ultra 64GB receives a C grade with 95.1 tok/s and 570K context.

What context window can internlm3 8b instruct abliterated i1 use on Mac Studio M2 Ultra 64GB?

On Mac Studio M2 Ultra 64GB, internlm3 8b instruct abliterated i1 can safely use up to 570K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M2 Ultra 64GB as fast as VRAM for internlm3 8b instruct abliterated i1?

Not always. Mac Studio M2 Ultra 64GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for Mac Studio M2 Ultra 64GBSee all hardware for internlm3 8b instruct abliterated i1
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