Rlm Qwen3 30B A3b v0.1 — Hardware Requirements & GPU Compatibility
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Rlm Qwen3 30B A3b v0.1 is a 30B-parameter open language model from mit-oasys in the Qwen 3 family. At Q4_K_M it needs about 19.80 GB of VRAM — see which GPUs and Macs can run it below.
Usable on a 24 GB GPU
Runs on 24 GB (Q4_K_M · ~177 tok/s · 16K)Macs from 24 GB (Q4_K_M · ~53 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Rlm Qwen3 30B A3b v0.1
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve mit-oasys/rlm-qwen3-30b-a3b-v0.1 --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX PRO 4500 (32 GB) (8 GB headroom) · Q4_K_M
- Generation speed
- ~166 tok/s
- generation speed
- Cost per 1M output tokens
- $0.57
- per 1M output tokens
How Much VRAM Does Rlm Qwen3 30B A3b v0.1 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 14.0 GB | 22 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~209 tok/s | |
| 16.1 GB | 24.1 GB | 3-bit medium — 3-bit medium quantization | Fits: 16K context · ~196 tok/s | |
| 19.8 GB | 27.8 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 16K context · ~177 tok/s | |
| 23.5 GB | 31.5 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~161 tok/s | |
| 27.2 GB | 35.2 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 31 GB | |
| 33 GB | 41 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 36.7 GB | |
| 66 GB | 74 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Does not fit: needs 69.7 GB |
Selected Q4_K_M
4-bit medium quantization23.5GBat 16K context
- Weights
- 19.0 GB
- KV cache
- 4.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 0.5 GB free · ~177 tok/s
≥50 tok/s needs ≥175 GB/s · RTX 5090 ~230, RTX 3090 Ti ~177
Which Hardware Can Run Rlm Qwen3 30B A3b v0.1?
Q4_K_M · 19.8 GBRlm Qwen3 30B A3b v0.1 (Q4_K_M) requires 19.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 26+ GB is recommended. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 122 devices with unified memory can run Rlm Qwen3 30B A3b v0.1, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M1 Max (32 GB).
20 GB
tight · up to 2K ctx
1 GPU
RX 7900 XT~73Simulate output on RX 7900 XT32–40 GB
fits · 8.5 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~230Simulate output on RTX 5090M5 Max36 GB~64Simulate output on Mac Studio M5 Max (36 GB)M532 GB~36Simulate output on MacBook Air 13" M5 (32 GB)Orin~57Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~37Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Rlm Qwen3 30B A3b v0.1
Qwen 3 family · 79 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3.8 27BAlibaba | 122.0 | 17.9 GB | ~38 | same size |
| Hy Memory 30B A3BTencent | — | 19.1 GB | ~184 | same size |
| Granite Switch 4.1 30BIBM | — | 22.7 GB | ~35 | same size |
| Llm Jp 4.1 32B A3b Thinkingllm-jp | — | 20.1 GB | ~188 | same size |
| Muse Glimmer 30BMeta | — | 19.6 GB | ~36 | same size |
| NVIDIA Nemotron 3.5 Lightning 30B A3B BF16NVIDIA | — | 19.3 GB | ~197 | same size |
Specifications
- Publisher
- mit-oasys
- Family
- Qwen 3
- Parameters
- 30B
- Active parameters
- 3B per token
- Release Date
- 2026-05-24
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does Rlm Qwen3 30B A3b v0.1 need?
Rlm Qwen3 30B A3b v0.1 requires 19.8 GB of VRAM at Q4_K_M, or 66 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 30B × 4.8 bits ÷ 8 = 18 GB
KV Cache + Overhead ≈ 1.8 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Rlm Qwen3 30B A3b v0.1?
Yes, at Q5_K_M (23.5 GB) or lower. Higher quantizations like Q6_K (27.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Rlm Qwen3 30B A3b v0.1?
For Rlm Qwen3 30B A3b v0.1, Q4_K_M (19.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (23.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 14.0 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Rlm Qwen3 30B A3b v0.1 on a Mac?
Yes — Mac Mini M5 Pro (24 GB) and 106 other Macs can run Rlm Qwen3 30B A3b v0.1. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 19.8 GB of usable unified memory (RAM minus macOS overhead).
Can I run Rlm Qwen3 30B A3b v0.1 locally?
Yes — Rlm Qwen3 30B A3b v0.1 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Rlm Qwen3 30B A3b v0.1?
At Q4_K_M, Rlm Qwen3 30B A3b v0.1 can reach ~100 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~177 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 2.0 GB active ÷ (8000 × 0.65) = 0.38 ms, plus 48 layers × 0.055 ms = 2.64 ms, so 1000 ÷ 3.02 ms = ~331 tok/s
Estimated speed at Q4_K_M (19.8 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Rlm Qwen3 30B A3b v0.1?
At Q4_K_M, the download is about 18.00 GB. The full-precision BF16 version is 60.00 GB. The smallest option (Q2_K) is 12.75 GB.
Which GPUs can run Rlm Qwen3 30B A3b v0.1?
8 consumer GPUs can run Rlm Qwen3 30B A3b v0.1 at Q4_K_M. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU has plenty of headroom for comfortable inference.
Which devices can run Rlm Qwen3 30B A3b v0.1?
122 devices with unified memory can run Rlm Qwen3 30B A3b v0.1 at Q4_K_M (19.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.