UserRepGen 4B — Hardware Requirements & GPU Compatibility
ChatVisionFunctions
UserRepGen 4B is a 4.5B-parameter open language model from Microsoft. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 3.19 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 · 128K)Best on 24 GB (Q4_K_M · ~205 tok/s · 256K)Macs from 8 GB (Q4_K_M · ~23 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run UserRepGen 4B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve microsoft/UserRepGen-4B --max-model-len 16384vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (8 GB headroom) · Q4_K_M
- Generation speed
- ~73 tok/s
- generation speed
- Cost per 1M output tokens
- $0.27
- per 1M output tokens
How Much VRAM Does UserRepGen 4B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.4 GB | 2.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~273 tok/s | |
| 2.7 GB | 3.2 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~245 tok/s | |
| 3.2 GB | 3.7 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 256K context · ~205 tok/s | |
| 3.7 GB | 4.2 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 256K context · ~177 tok/s | |
| 4.2 GB | 4.7 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 256K context · ~156 tok/s | |
| 5.0 GB | 5.5 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 256K context · ~131 tok/s | |
| 9.6 GB | 10.1 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 256K context · ~69 tok/s |
Selected Q4_K_M
4-bit medium quantization3.4GBat 16K context
- Weights
- 2.7 GB
- KV cache
- 0.3 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 20.6 GB free · ~205 tok/s
≥50 tok/s needs ≥245 GB/s · RTX 5090 ~365, RTX 3090 Ti ~205
Which Hardware Can Run UserRepGen 4B?
Q4_K_M · 3.2 GBUserRepGen 4B (Q4_K_M) requires 3.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 5+ GB is recommended. Using the full 262K context window can add up to 5.2 GB, bringing total usage to 8.4 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run UserRepGen 4B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), MacBook Air 13" M3 (8 GB).
≤ 11 GB
≤ 11 GB tier →fits · 4.6 GB free · up to 128K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~155Simulate output on RTX 3080RTX 2080 Ti~126Simulate output on RTX 2080 TiRTX 3070 Ti~124Simulate output on RTX 3070 TiM3*8 GB~23Simulate output on iMac 24" M3 (8 GB)Orin Nano~21Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~15Simulate output on iPhone 1720 GB
fits · 16.6 GB free · up to 256K ctx
1 GPU
RX 7900 XT~151Simulate output on RX 7900 XT32–40 GB
fits · 28.6 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~365Simulate output on RTX 5090M5 Max36 GB~101Simulate output on Mac Studio M5 Max (36 GB)M532 GB~34Simulate output on MacBook Air 13" M5 (32 GB)Orin~42Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~28Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Gemma 3 4B ITGoogle | 64.0 | 5 GB | ~231 | same size |
| Qwen3.5 4BAlibaba | — | 3.4 GB | ~201 | same size |
| Translategemma 4B ITGoogle | — | 5 GB | ~231 | same size |
| Molmo2 4BAllen AI | — | 4.7 GB | ~193 | same size |
| Qwen3 VL 4B InstructAlibaba | — | 4.5 GB | ~208 | same size |
| Agents A1 4BInternScience | — | 3.4 GB | ~205 | same size |
Specifications
- Publisher
- Microsoft
- Parameters
- 4.5B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-10-14
- License
- MIT
Frequently Asked Questions
How much VRAM does UserRepGen 4B need?
UserRepGen 4B requires 3.2 GB of VRAM at Q4_K_M, or 9.6 GB at BF16. Full 262K context adds up to 5.2 GB (8.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.5B × 4.8 bits ÷ 8 = 2.7 GB
KV Cache + Overhead ≈ 0.5 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.
KV Cache + Overhead ≈ 5.7 GB (at full 262K context)
VRAM usage by quantization
What's the best quantization for UserRepGen 4B?
For UserRepGen 4B, Q4_K_M (3.2 GB) offers the best balance of quality and VRAM usage. Q5_K_M (3.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.4 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run UserRepGen 4B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run UserRepGen 4B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 3.2 GB of usable unified memory (RAM minus macOS overhead).
Can I run UserRepGen 4B locally?
Yes — UserRepGen 4B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is UserRepGen 4B?
At Q4_K_M, UserRepGen 4B can reach ~1505 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~205 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 ÷ 3.19 × 0.65 = ~1630 tok/s
Estimated speed at Q4_K_M (3.2 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of UserRepGen 4B?
At Q4_K_M, the download is about 2.72 GB. The full-precision BF16 version is 9.08 GB. The smallest option (Q2_K) is 1.93 GB.
Which GPUs can run UserRepGen 4B?
52 consumer GPUs can run UserRepGen 4B 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 UserRepGen 4B?
163 devices with unified memory can run UserRepGen 4B at Q4_K_M (3.2 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.