Kimi Linear 48B A3B needs ~35.2 GB VRAM. AMD Instinct MI100 32GB has 32.0 GB. With Q4_K_M quantization, expect ~17 tok/s.
Operating mode
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
3.2 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~2.7 GB host RAM)
Decode
16.7 tok/s
TTFT
11578 ms
Safe context
4K
Memory
35.2 GB / 32.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Very compromised (needs ~2.3 GB host RAM) | 17.2 tok/s | 6141 ms | 4K |
| Coding | A | Very compromised | 16.7 tok/s | 11578 ms | 4K |
| Agentic Coding | A | Very compromised (needs ~3.3 GB host RAM) | 15.8 tok/s | 17787 ms | 4K |
| Reasoning | A | Very compromised (needs ~2.7 GB host RAM) | 16.7 tok/s | 13683 ms | 4K |
| RAG | A | Very compromised (needs ~3.3 GB host RAM) | 15.8 tok/s |
How Kimi Linear 48B A3B (48B params) fits at each quantization level on AMD Instinct MI100 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 18.7 GB | Low | A81 |
Q3_K_SBest for your GPU | 3 | 23.5 GB | Low | A81 |
Copy-paste commands to run Kimi Linear 48B A3B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "moonshotai/Kimi-Linear-48B-A3B-Instruct" \
--hf-file "Kimi-Linear-48B-A3B-Instruct-Q4_K_M.gguf" \
-c 4096 -ngl 99Yes, AMD Instinct MI100 32GB can run Kimi Linear 48B A3B with a A grade (Very compromised). Expected decode speed: 16.7 tok/s.
Kimi Linear 48B A3B (48B parameters) requires approximately 35.2 GB of memory with Q4_K_M quantization.
The recommended quantization for Kimi Linear 48B A3B is Q4_K_M, which balances quality and memory efficiency.
On AMD Instinct MI100 32GB, Kimi Linear 48B A3B achieves approximately 16.7 tokens per second decode speed with a time-to-first-token of 11578ms using Q4_K_M quantization.
For coding workloads, Kimi Linear 48B A3B on AMD Instinct MI100 32GB receives a A grade with 16.7 tok/s and 4K context.
On AMD Instinct MI100 32GB, Kimi Linear 48B A3B can safely use up to 4K tokens of context. The model's official context limit is 1.0M, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/kimi-linear-48b-a3b-on-instinct-mi100-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 22234 ms |
| 4K |
| 4 |
26.9 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 29.3 GB | Medium | F0 |
Q5_K_M | 5 | 34.6 GB | High | F0 |
Q6_K | 6 | 39.4 GB | High | F0 |
Q8_0 | 8 | 51.4 GB | Very High | F0 |
F16 | 16 | 98.4 GB | Maximum | F0 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.