Codegen 350M Mono — Hardware Requirements & GPU Compatibility
ChatCode
Codegen 350M Mono is a 350M-parameter open language model from Salesforce. It supports a context window of up to 2,048 tokens. At FP16 it needs about 0.77 GB of VRAM — see which GPUs and Macs can run it below.
123.3K downloads
101 likesSpecifications
- Publisher
- Salesforce
- Parameters
- 350M
- Architecture
- CodeGenForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 51,200
- Release Date
- 2022-04-11
- License
- BSD 3-Clause
Get Started
HuggingFace
How Much VRAM Does Codegen 350M Mono Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| FP16est. | 16.00 | 0.8 GB | — | 0.70 GB | Full half-precision — baseline for inference |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Codegen 350M Mono?
FP16 · 0.8 GBCodegen 350M Mono (FP16) requires 0.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Codegen 350M Mono?
FP16 · 0.8 GB59 devices with unified memory can run Codegen 350M Mono, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does Codegen 350M Mono need?
Codegen 350M Mono requires 0.8 GB of VRAM at FP16.
VRAM = Weights + KV Cache + Overhead
Weights = 350M × 16 bits ÷ 8 = 0.7 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
FP160.8 GB- Can I run Codegen 350M Mono on a Mac?
Codegen 350M Mono requires at least 0.8 GB at FP16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Codegen 350M Mono locally?
Yes — Codegen 350M Mono can run locally on consumer hardware. At FP16 quantization it needs 0.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Codegen 350M Mono?
At FP16, Codegen 350M Mono can reach ~6234 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~851 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 ÷ 0.8 × 0.65 = ~6753 tok/s
Estimated speed at FP16 (0.8 GB)
~6753 tok/s~851 tok/s~6753 tok/s~6234 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Codegen 350M Mono?
At FP16, the download is about 0.70 GB.
- Which GPUs can run Codegen 350M Mono?
52 consumer GPUs can run Codegen 350M Mono at FP16 (0.8 GB). 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 Codegen 350M Mono?
59 devices with unified memory can run Codegen 350M Mono at FP16 (0.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.