HRM Text 1B Chat — Hardware Requirements & GPU Compatibility
Chat
HRM Text 1B Chat is a 1.2B-parameter open language model from taykso. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 2.62 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 · 4K)Best on 24 GB (Q4_K_M · ~250 tok/s · 4K)Macs from 8 GB (Q4_K_M · ~27 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run HRM Text 1B Chat
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve taykso/HRM-Text-1B-Chat --max-model-len 4096vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (7 GB headroom) · Q4_K_M
- Generation speed
- ~89 tok/s
- generation speed
- Cost per 1M output tokens
- $0.24
- per 1M output tokens
How Much VRAM Does HRM Text 1B Chat Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 2.4 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 4K context · ~272 tok/s | |
| 2.5 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 4K context · ~263 tok/s | |
| 2.6 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 4K context · ~250 tok/s | |
| 2.8 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 4K context · ~238 tok/s | |
| 2.9 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 4K context · ~227 tok/s | |
| 3.1 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 4K context · ~212 tok/s | |
| 4.3 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 4K context · ~153 tok/s |
Selected Q4_K_M
4-bit medium quantization4.2GBat 4K context
- Weights
- 0.7 GB
- KV cache
- 3.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 19.8 GB free · ~250 tok/s
≥50 tok/s needs ≥201 GB/s · RTX 5090 ~445, RTX 3090 Ti ~250
Which Hardware Can Run HRM Text 1B Chat?
Q4_K_M · 2.6 GBHRM Text 1B Chat (Q4_K_M) requires 2.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 4K context window can add up to 1.6 GB, bringing total usage to 4.2 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run HRM Text 1B Chat, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 3.8 GB free · up to 4K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~189Simulate output on RTX 3080RTX 2080 Ti~153Simulate output on RTX 2080 TiRTX 3070 Ti~151Simulate output on RTX 3070 TiM3*8 GB~27Simulate output on iMac 24" M3 (8 GB)Orin Nano~25Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~18Simulate output on iPhone 1720 GB
fits · 15.8 GB free · up to 4K ctx
1 GPU
RX 7900 XT~183Simulate output on RX 7900 XT32–40 GB
fits · 27.8 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~445Simulate output on RTX 5090M5 Max36 GB~123Simulate output on Mac Studio M5 Max (36 GB)M532 GB~41Simulate output on MacBook Air 13" M5 (32 GB)Orin~51Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~34Simulate 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 |
|---|---|---|---|---|
| Llama 3.2 1B InstructMeta | — | 2.3 GB | ~799 | same size |
| TinyLlama 1.1B Chat v1.0TinyLlama | — | 1 GB | ~649 | same size |
| LFM2.5 1.2B ThinkingLiquid AI | — | 1.2 GB | ~612 | same size |
| LFM2 1.2BLiquid AI | — | 1.2 GB | ~612 | same size |
| T5gemma L L Ul2 ITGoogle | — | 4.3 GB | ~240 | same size |
| OLMo 1B HFAllen AI | — | 1.3 GB | ~516 | same size |
Specifications
- Publisher
- taykso
- Parameters
- 1.2B
- Architecture
- HrmTextForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 65,536
- Release Date
- 2026-05-28
- License
- Apache 2.0
Frequently Asked Questions
How much VRAM does HRM Text 1B Chat need?
HRM Text 1B Chat requires 2.6 GB of VRAM at Q4_K_M, or 4.3 GB at BF16. Full 4K context adds up to 1.6 GB (4.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.2B × 4.8 bits ÷ 8 = 0.7 GB
KV Cache + Overhead ≈ 1.9 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 ≈ 3.5 GB (at full 4K context)
VRAM usage by quantization
What's the best quantization for HRM Text 1B Chat?
For HRM Text 1B Chat, Q4_K_M (2.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (2.8 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 HRM Text 1B Chat on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run HRM Text 1B Chat. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 2.6 GB of usable unified memory (RAM minus macOS overhead).
Can I run HRM Text 1B Chat locally?
Yes — HRM Text 1B Chat can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is HRM Text 1B Chat?
At Q4_K_M, HRM Text 1B Chat can reach ~1832 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~250 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 ÷ 2.62 × 0.65 = ~1985 tok/s
Estimated speed at Q4_K_M (2.6 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of HRM Text 1B Chat?
At Q4_K_M, the download is about 0.71 GB. The full-precision BF16 version is 2.37 GB. The smallest option (Q2_K) is 0.50 GB.
Which GPUs can run HRM Text 1B Chat?
52 consumer GPUs can run HRM Text 1B Chat 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 HRM Text 1B Chat?
163 devices with unified memory can run HRM Text 1B Chat at Q4_K_M (2.6 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.