Rwkv 4 169M Pile — Hardware Requirements & GPU Compatibility
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Rwkv 4 169M Pile is a 169M-parameter open language model from RWKV. At Q4_K_M it needs about 0.11 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 · 32K)Best on 24 GB (Q4_K_M · ~5956 tok/s · 32K)Macs from 8 GB (Q4_K_M · ~652 tok/s)
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Rwkv 4 169M Pile
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve RWKV/rwkv-4-169m-pile --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 (11 GB headroom) · Q4_K_M
- Generation speed
- ~2127 tok/s
- generation speed
- Cost per 1M output tokens
- $0.011
- per 1M output tokens
How Much VRAM Does Rwkv 4 169M Pile Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.1 GB | 2 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~8190 tok/s | |
| 0.1 GB | 2 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~7280 tok/s | |
| 0.1 GB | 2 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~5956 tok/s | |
| 0.1 GB | 2 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~5040 tok/s | |
| 0.1 GB | 2 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~4368 tok/s | |
| 0.2 GB | 2.1 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~3448 tok/s | |
| 0.4 GB | 2.2 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 32K context · ~1771 tok/s |
Selected Q4_K_M
4-bit medium quantization1.0GBat 16K context
- Weights
- 0.1 GB
- KV cache
- 0.6 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.0 GB free · ~5956 tok/s
≥50 tok/s needs ≥8 GB/s · RTX 5090 ~10589, RTX 3090 Ti ~5956
Which Hardware Can Run Rwkv 4 169M Pile?
Q4_K_M · 0.1 GBRwkv 4 169M Pile (Q4_K_M) requires 0.1 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 Rwkv 4 169M Pile, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.0 GB free · up to 32K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~4493Simulate output on RTX 3080RTX 2080 Ti~3640Simulate output on RTX 2080 TiRTX 3070 Ti~3595Simulate output on RTX 3070 TiM3*8 GB~652Simulate output on iMac 24" M3 (8 GB)Orin Nano~603Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~434Simulate output on iPhone 1720 GB
fits · 19.0 GB free · up to 32K ctx
1 GPU
RX 7900 XT~4364Simulate output on RX 7900 XT32–40 GB
fits · 31.0 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~10589Simulate output on RTX 5090M5 Max36 GB~2927Simulate output on Mac Studio M5 Max (36 GB)M532 GB~978Simulate output on MacBook Air 13" M5 (32 GB)Orin~1210Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~798Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
1| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Llama 160MJackFram | — | 0.5 GB | ~1394 | same size |
Specifications
- Publisher
- RWKV
- Parameters
- 169M
- Architecture
- RwkvForCausalLM
- Vocabulary Size
- 50,277
- Release Date
- 2023-05-04
Frequently Asked Questions
How much VRAM does Rwkv 4 169M Pile need?
Rwkv 4 169M Pile requires 0.1 GB of VRAM at Q4_K_M, or 0.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 169M × 4.8 bits ÷ 8 = 0.1 GB
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 Rwkv 4 169M Pile?
For Rwkv 4 169M Pile, Q4_K_M (0.1 GB) offers the best balance of quality and VRAM usage. Q5_K_M (0.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.1 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Rwkv 4 169M Pile on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Rwkv 4 169M Pile. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 0.1 GB of usable unified memory (RAM minus macOS overhead).
Can I run Rwkv 4 169M Pile locally?
Yes — Rwkv 4 169M Pile can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Rwkv 4 169M Pile?
At Q4_K_M, Rwkv 4 169M Pile can reach ~43636 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~5956 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 ÷ 0.11 × 0.65 = ~47273 tok/s
Estimated speed at Q4_K_M (0.1 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Rwkv 4 169M Pile?
At Q4_K_M, the download is about 0.10 GB. The full-precision BF16 version is 0.34 GB. The smallest option (Q2_K) is 0.07 GB.
Which GPUs can run Rwkv 4 169M Pile?
52 consumer GPUs can run Rwkv 4 169M Pile 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 Rwkv 4 169M Pile?
163 devices with unified memory can run Rwkv 4 169M Pile at Q4_K_M (0.1 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.