Riva Translate 4B Instruct — Hardware Requirements & GPU Compatibility
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Riva Translate 4B Instruct is a 4.2B-parameter open language model from NVIDIA. It supports a context window of up to 8,192 tokens. At BF16 it needs about 8.87 GB of VRAM — see which GPUs and Macs can run it below.
131 downloads
18 likesSpecifications
- Publisher
- NVIDIA
- Parameters
- 4.2B
- Architecture
- MistralForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2025-12-10
- License
- Other
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HuggingFace
How Much VRAM Does Riva Translate 4B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 8.9 GB | 9.5 GB | 8.36 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Riva Translate 4B Instruct?
BF16 · 8.9 GBRiva Translate 4B Instruct (BF16) requires 8.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 12+ GB is recommended. Using the full 8K context window can add up to 0.7 GB, bringing total usage to 9.5 GB. 28 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Riva Translate 4B Instruct?
BF16 · 8.9 GB27 devices with unified memory can run Riva Translate 4B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Riva Translate 4B Instruct need?
Riva Translate 4B Instruct requires 8.9 GB of VRAM at BF16. Full 8K context adds up to 0.7 GB (9.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.2B × 16 bits ÷ 8 = 8.4 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.1 GB (at full 8K context)
VRAM usage by quantization
BF168.9 GBBF16 + full context9.5 GB- Can I run Riva Translate 4B Instruct on a Mac?
Riva Translate 4B Instruct requires at least 8.9 GB at BF16, 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 Riva Translate 4B Instruct locally?
Yes — Riva Translate 4B Instruct can run locally on consumer hardware. At BF16 quantization it needs 8.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Riva Translate 4B Instruct?
At BF16, Riva Translate 4B Instruct can reach ~329 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~74 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 8.9 × 0.55 = ~329 tok/s
Estimated speed at BF16 (8.9 GB)
~329 tok/s~74 tok/s~246 tok/s~203 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Riva Translate 4B Instruct?
At BF16, the download is about 8.36 GB.
- Which GPUs can run Riva Translate 4B Instruct?
28 consumer GPUs can run Riva Translate 4B Instruct at BF16 (8.9 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 17 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Riva Translate 4B Instruct?
27 devices with unified memory can run Riva Translate 4B Instruct at BF16 (8.9 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.