MicroLlama v2 — Hardware Requirements & GPU Compatibility
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MicroLlama v2 is a 45M-parameter open language model from ViorikaAI-org in the Llama family. It supports a context window of up to 2,048 tokens. At Q4_K_M it needs about 0.34 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 2K)Best on 24 GB (Q4_K_M · ~1927 tok/s · 2K)Macs from 8 GB (Q4_K_M · ~211 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run MicroLlama v2
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve ViorikaAI-org/MicroLlama-v2 --max-model-len 2048vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (11 GB headroom) · Q4_K_M
- Generation speed
- ~688 tok/s
- generation speed
- Cost per 1M output tokens
- $0.028
- per 1M output tokens
How Much VRAM Does MicroLlama v2 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.3 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 2K context · ~1927 tok/s | |
| 0.3 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 2K context · ~1927 tok/s | |
| 0.3 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 2K context · ~1927 tok/s | |
| 0.3 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 2K context · ~1872 tok/s | |
| 0.3 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 2K context · ~1872 tok/s | |
| 0.4 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 2K context · ~1820 tok/s | |
| 0.4 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 2K context · ~1598 tok/s |
Selected Q4_K_M
4-bit medium quantization0.3GBat 2K context
- Weights
- 0.1 GB
- KV cache
- 0.0 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.7 GB free · ~1927 tok/s
≥50 tok/s needs ≥26 GB/s · RTX 5090 ~3426, RTX 3090 Ti ~1927
Which Hardware Can Run MicroLlama v2?
Q4_K_M · 0.3 GBMicroLlama v2 (Q4_K_M) requires 0.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run MicroLlama v2, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.7 GB free · up to 2K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~1454Simulate output on RTX 3080RTX 2080 Ti~1178Simulate output on RTX 2080 TiRTX 3070 Ti~1163Simulate output on RTX 3070 TiM3*8 GB~211Simulate output on iMac 24" M3 (8 GB)Orin Nano~195Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~140Simulate output on iPhone 1720 GB
fits · 19.7 GB free · up to 2K ctx
1 GPU
RX 7900 XT~1412Simulate output on RX 7900 XT32–40 GB
fits · 31.7 GB free · up to 2K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~3426Simulate output on RTX 5090M5 Max36 GB~947Simulate output on Mac Studio M5 Max (36 GB)M532 GB~316Simulate output on MacBook Air 13" M5 (32 GB)Orin~392Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~258Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to MicroLlama v2
Llama family · 19 models →Specifications
- Publisher
- ViorikaAI-org
- Family
- Llama
- Parameters
- 45M
- Architecture
- LlamaForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2026-07-05
- License
- MIT
Frequently Asked Questions
How much VRAM does MicroLlama v2 need?
MicroLlama v2 requires 0.3 GB of VRAM at Q4_K_M, or 0.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 45M × 4.8 bits ÷ 8 = 0 GB
KV Cache + Overhead ≈ 0.3 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
What's the best quantization for MicroLlama v2?
For MicroLlama v2, Q4_K_M (0.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.3 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run MicroLlama v2 on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run MicroLlama v2. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.3 GB of usable unified memory (RAM minus macOS overhead).
Can I run MicroLlama v2 locally?
Yes — MicroLlama v2 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is MicroLlama v2?
At Q4_K_M, MicroLlama v2 can reach ~14118 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1927 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 0.34 × 0.65 = ~15294 tok/s
Estimated speed at Q4_K_M (0.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of MicroLlama v2?
At Q4_K_M, the download is about 0.03 GB. The full-precision BF16 version is 0.09 GB. The smallest option (Q2_K) is 0.02 GB.
Which GPUs can run MicroLlama v2?
52 consumer GPUs can run MicroLlama v2 at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run MicroLlama v2?
163 devices with unified memory can run MicroLlama v2 at Q4_K_M (0.3 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.