Recursive Language Model 198M — Hardware Requirements & GPU Compatibility
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Recursive Language Model 198M is a 198M-parameter open language model from Girinath11. It supports a context window of up to 512 tokens. At BF16 it needs about 0.44 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 8 GB (BF16)Best on 24 GB (BF16 · ~1489 tok/s)Macs from 8 GB (BF16 · ~163 tok/s)
MIT
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Recursive Language Model 198M
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve Girinath11/recursive-language-model-198m --max-model-len 512vLLM 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) · BF16
- Generation speed
- ~532 tok/s
- generation speed
- Cost per 1M output tokens
- $0.043
- per 1M output tokens
How Much VRAM Does Recursive Language Model 198M Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.4 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: — context · ~1489 tok/s |
Selected BF16
Brain floating point 160.4GBat 1K context
- Weights
- 0.1 GB
- KV cache
- 0.0 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 23.6 GB free · ~1489 tok/s
≥50 tok/s needs ≥34 GB/s · RTX 5090 ~2647, RTX 3090 Ti ~1489
Which Hardware Can Run Recursive Language Model 198M?
BF16 · 0.4 GBRecursive Language Model 198M (BF16) requires 0.4 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 Recursive Language Model 198M, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 7.6 GB free · up to — ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~1123Simulate output on RTX 3080RTX 2080 Ti~910Simulate output on RTX 2080 TiRTX 3070 Ti~899Simulate output on RTX 3070 TiM3*8 GB~163Simulate output on iMac 24" M3 (8 GB)Orin Nano~151Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~109Simulate output on iPhone 1720 GB
fits · 19.6 GB free · up to — ctx
1 GPU
RX 7900 XT~1091Simulate output on RX 7900 XT32–40 GB
fits · 31.6 GB free · up to — ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~2647Simulate output on RTX 5090M5 Max36 GB~732Simulate output on Mac Studio M5 Max (36 GB)M532 GB~244Simulate output on MacBook Air 13" M5 (32 GB)Orin~303Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~199Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at BF16
Specifications
- Publisher
- Girinath11
- Parameters
- 198M
- Architecture
- RecursiveLanguageModel
- Context Length
- 512 tokens
- Vocabulary Size
- 50,260
- Release Date
- 2026-01-10
- License
- MIT
Frequently Asked Questions
How much VRAM does Recursive Language Model 198M need?
Recursive Language Model 198M requires 0.4 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 198M × 16 bits ÷ 8 = 0.4 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
Can I run Recursive Language Model 198M on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Recursive Language Model 198M. Apple Silicon uses unified memory, so the model shares RAM with the system. At BF16 you need at least 0.4 GB of usable unified memory (RAM minus macOS overhead).
Can I run Recursive Language Model 198M locally?
Yes — Recursive Language Model 198M can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Recursive Language Model 198M?
At BF16, Recursive Language Model 198M can reach ~10909 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1489 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 ÷ 0.44 × 0.65 = ~11818 tok/s
Estimated speed at BF16 (0.4 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Recursive Language Model 198M?
At BF16, the download is about 0.40 GB.
Which GPUs can run Recursive Language Model 198M?
52 consumer GPUs can run Recursive Language Model 198M at BF16. 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 Recursive Language Model 198M?
163 devices with unified memory can run Recursive Language Model 198M at BF16 (0.4 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.