Steelskull·LlamaForCausalLM

L3.3 MS Nevoria 70B — Hardware Requirements & GPU Compatibility

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L3.3 MS Nevoria 70B is a 70.6B-parameter open language model from Steelskull. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 43.30 GB of VRAM — see which GPUs and Macs can run it below.

135 downloads 176 likes131K context

Specifications

Publisher
Steelskull
Parameters
70.6B
Architecture
LlamaForCausalLM
Context Length
131,072 tokens
Vocabulary Size
128,256
Release Date
2025-01-14
License
Other

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How Much VRAM Does L3.3 MS Nevoria 70B Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_Kest.3.4031.0 GB
Q3_K_Mest.3.9035.4 GB
Q4_K_Mest.4.8043.3 GB
Q5_K_Mest.5.7051.2 GB
Q6_Kest.6.6059.2 GB
Q8_0est.8.0071.5 GB
BF16est.16.00142.1 GB

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 L3.3 MS Nevoria 70B?

Q4_K_M · 43.3 GB

L3.3 MS Nevoria 70B (Q4_K_M) requires 43.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 57+ GB is recommended. Using the full 131K context window can add up to 42.3 GB, bringing total usage to 85.6 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.

Which Devices Can Run L3.3 MS Nevoria 70B?

Q4_K_M · 43.3 GB

27 devices with unified memory can run L3.3 MS Nevoria 70B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).

Related Models

Frequently Asked Questions

How much VRAM does L3.3 MS Nevoria 70B need?

L3.3 MS Nevoria 70B requires 43.3 GB of VRAM at Q4_K_M, or 142.1 GB at BF16. Full 131K context adds up to 42.3 GB (85.6 GB total).

VRAM = Weights + KV Cache + Overhead

Weights = 70.6B × 4.8 bits ÷ 8 = 42.3 GB

KV Cache + Overhead 1 GB (at 2K context + ~0.3 GB framework)

KV Cache + Overhead 43.3 GB (at full 131K context)

VRAM usage by quantization

43.3 GB
85.6 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 5090 run L3.3 MS Nevoria 70B?

Yes, at Q2_K (31.0 GB) or lower. Higher quantizations like Q3_K_M (35.4 GB) exceed the NVIDIA GeForce RTX 5090's 32 GB.

What's the best quantization for L3.3 MS Nevoria 70B?

For L3.3 MS Nevoria 70B, Q4_K_M (43.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (51.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 31.0 GB.

VRAM requirement by quantization

Q2_K
31.0 GB
Q4_K_M
43.3 GB
Q5_K_M
51.2 GB
Q6_K
59.2 GB
Q8_0
71.5 GB
BF16
142.1 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run L3.3 MS Nevoria 70B on a Mac?

L3.3 MS Nevoria 70B requires at least 31.0 GB at Q2_K, 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 L3.3 MS Nevoria 70B locally?

Yes — L3.3 MS Nevoria 70B can run locally on consumer hardware. At Q4_K_M quantization it needs 43.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is L3.3 MS Nevoria 70B?

At Q4_K_M, L3.3 MS Nevoria 70B can reach ~111 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.

tok/s = (bandwidth GB/s ÷ model GB) × efficiency

Example: NVIDIA B2008000 ÷ 43.3 × 0.65 = ~120 tok/s

Estimated speed at Q4_K_M (43.3 GB)

~120 tok/s
~120 tok/s
~111 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of L3.3 MS Nevoria 70B?

At Q4_K_M, the download is about 42.33 GB. The full-precision BF16 version is 141.11 GB. The smallest option (Q2_K) is 29.99 GB.

Which GPUs can run L3.3 MS Nevoria 70B?

No single consumer GPU has enough VRAM to run L3.3 MS Nevoria 70B at Q4_K_M (43.3 GB). Multi-GPU or professional hardware is required.

Which devices can run L3.3 MS Nevoria 70B?

27 devices with unified memory can run L3.3 MS Nevoria 70B at Q4_K_M (43.3 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), 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.