Home / Models / Mistral 7B Instruct v0.3 / NVIDIA RTX 4080 SUPER 16 GB

Calculated memory fit · performance is a separate section

Can Mistral 7B Instruct v0.3 run on NVIDIA RTX 4080 SUPER 16 GB?

Yes. Calculated memory fit: Mistral 7B Instruct v0.3 can run entirely in this GPU’s usable VRAM at one or more supported quantizations.

Can it run?

Can it run?: Yes, full GPU fit (calculated estimate).
Full GPU fit?: Yes
Model: Mistral 7B Instruct v0.3 · 7.3B
GPU: NVIDIA RTX 4080 SUPER 16 GB · 16 GB advertised

What else can NVIDIA RTX 4080 SUPER 16 GB run? · Find other GPUs for Mistral 7B Instruct v0.3

Memory calculation

Breakdown for Q4 at 8K context, batch size 1. Calculator v1.0.0.

Model weights	4.3 GB
KV cache	1.0 GB
Runtime reserve	0.7 GB
Safety margin	0.4 GB
Required (estimated)	6.3 GB
GPU usable VRAM	14.4 GB

Result: FITS IN VRAM

Quantization table

Quantization	2K	4K	8K	16K	32K
BF16	Offload	Offload	Offload	Offload	Offload
FP16	Offload	Offload	Offload	Offload	Offload
Q3	Fits	Fits	Fits	Fits	Fits
Q4	Fits	Fits	Fits	Fits	Fits
Q5	Fits	Fits	Fits	Fits	Fits
Q6	Fits	Fits	Fits	Fits	Fits
Q8	Fits	Fits	Fits	Fits	Fits

What fits entirely in VRAM?

Q3 at 16K context
Q3 at 2K context
Q3 at 32K context
Q3 at 4K context
Q3 at 8K context
Q4 at 16K context
Q4 at 2K context
Q4 at 32K context
Q4 at 4K context
Q4 at 8K context
Q5 at 16K context
Q5 at 2K context

What requires RAM offload?

Offload is shown only when the model does not fully fit in usable VRAM but stays within the modeled offload allowance.

Performance

Separate from memory fit. Runtime is llama.cpp · quantization bucket Q4 (GGUF Q4_0 maps to Q4). Workload is llama-bench pp512 / tg128 (short context, batch 1, full GPU offload). This is not a ranking and not a 8K–128K speed claim.

Model: Mistral 7B Instruct v0.3
GPU: NVIDIA RTX 4080 SUPER 16 GB
Runtime: llama.cpp · CUDA · model 1.0.0
Decode: ~123 tok/s · Calibrated estimate · HIGH confidence (spread ~109 tok/s–~138 tok/s)
Prefill: ~8775 tok/s at a 512-token prompt · Calibrated estimate · MEDIUM confidence
Time to first token: ~58 ms for a 512-token prompt (512 / prefill tok/s). Not estimated at 8K–128K.

decode_tps = GPU_efficiency × memory_bandwidth / model_weight_bytes; efficiency from same-gpu Llama 2 7B Q4_0 efficiency

Performance methodology · Performance explorer

Available graphics cards

These SKUs use this GPU chip. Amazon CTAs appear only for EXACT/HIGH matches. Absence of a price does not change the compatibility result above.

Check price on Amazon

GIGABYTE

GIGABYTE GeForce RTX 4080 SUPER WINDFORCE V2 16G NVIDIA 16 GB GDDR6X

16 GB · Amazon EXACT

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ASUS

ASUS TUF Gaming TUF-RTX4080S-O16G-GAMING NVIDIA GeForce RTX 4080 SUPER 16 GB GDDR6X

16 GB · Amazon EXACT

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GIGABYTE

GIGABYTE GAMING GeForce RTX 4080 SUPER OC 16G NVIDIA 16 GB GDDR6X

16 GB · Amazon EXACT

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GIGABYTE

GIGABYTE AERO GeForce RTX 4080 SUPER OC 16G NVIDIA 16 GB GDDR6X

16 GB · Amazon EXACT

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MSI

MSI SUPRIM GeForce RTX 4080 SUPER 16G X NVIDIA 16 GB GDDR6X

16 GB · Amazon EXACT

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ASUS

ASUS ProArt -RTX4080S-O16G NVIDIA GeForce RTX 4080 SUPER 16 GB GDDR6X

16 GB · Amazon EXACT

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ASUS

ASUS ROG -STRIX-RTX4080S-O16G-GAMING NVIDIA GeForce RTX 4080 SUPER 16 GB GDDR6X

16 GB · Amazon EXACT

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ASUS

ASUS TUF Gaming TUF-RTX4080S-16G-GAMING NVIDIA GeForce RTX 4080 SUPER 16 GB GDDR6X

16 GB · Amazon unmatched

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Sources

Model: official config mistralai/Mistral-7B-Instruct-v0.3 (main). GPU/product specs: Icecat. Compatibility: RigForAI calculator v1.0.0 estimated VRAM · Q8 at 8K: FITS_IN_VRAM. Amazon: affiliate commerce match, not a spec source.