VRAM Reference

Llama 3.1 8B Instruct VRAM Requirements: FP16 / INT4 / KV-Cache (2026)

Llama 3.1 8B Instruct runs on 8.03B parameters with a 128K-token context window. Weights are deterministic param math; KV-cache uses the registry layer geometry where published, otherwise a modeled GQA estimate — every figure is provenance-labeled below.

FP16 weights16 GB

INT4 weights6 GB

Context128K tokens

Single-GPU verdictFits (INT4, 15 GB)

16 GB FP16 weights

Calculated EstimateHIGH

SourceOpenGPU Radar models-registry.json (param-count weight model)

VerifiedSep 29, 2026

Value

16 GB

Methodology

weights = parametersB × bytes-per-parameter (FP16 = 2 B, INT4 = 0.5 B)

Refreshed daily from provider APIs and market scrapingSep 29, 2026

6 GB INT4 weights

Calculated EstimateHIGH

SourceOpenGPU Radar models-registry.json (param-count weight model)

VerifiedSep 29, 2026

Value

6 GB

Methodology

weights = parametersB × bytes-per-parameter (FP16 = 2 B, INT4 = 0.5 B)

Refreshed daily from provider APIs and market scrapingSep 29, 2026

128K context window

Manufacturer SpecHIGH

SourceModel vendor context-window specification

VerifiedSep 29, 2026

Value

128,000 tokens

Refreshed daily from provider APIs and market scrapingSep 29, 2026

Methodology →

How much VRAM does Llama 3.1 8B Instruct need?

Weights: 16 GB at FP16, 6 GB at INT4 (parameters × bytes-per-parameter: FP16 = 2 B, INT4 = 0.5 B).

Full stack at 128K: 36.6 GB FP16 / 14.8 GB INT4 — including KV-cache, CUDA overhead, activations, and fragmentation headroom from the canonical VRAM engine.

Single-GPU verdict: INT4 fits in 24 GB class cards (e.g. RTX 4090); FP16 needs 80 GB-class hardware.

VRAM by Precision & Context

From the canonical VRAM engine: weights + KV-cache + CUDA overhead + activations + fragmentation headroom. KV-cache uses published layer geometry where available; otherwise a modeled GQA estimate (labeled in the badge).

Precision	Context	Weights	KV-Cache	Total (est.)
FP16	4K	16.1 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 16.06 GB Methodology Formula: parameterCountB × bytesPerParam. Precision: FP16 = 2 bytes/param. (parameterCountB in billions, result in GB). Assumptions & Parameters paramCountB: 8.03 quantization: fp16 bytesPerParam: 2 Refreshed daily from provider APIs and market scrapingSep 26, 2026	0.50 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 0.5 GB Methodology GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1. Assumptions & Parameters numLayers: 32 numKvHeads: 8 headDim: 128 contextLength: 4096 batchSize: 1 quantization: fp16 gqaRatio: 4 Refreshed daily from provider APIs and market scrapingSep 26, 2026	20.0 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 19.98 GB Methodology Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%). Assumptions & Parameters paramCountB: 8.03 quantization: fp16 contextLength: 4096 batchSize: 1 numLayers: 32 numKvHeads: 8 headDim: 128 gqaRatio: 4 architecture: llama-3.1-8b bytesPerParam: 2 bytesPerKvElement: 2 cudaOverheadGb: 1.2 activationGb: 0.4 fragmentationHeadroomPct: 10 isMla: false Refreshed daily from provider APIs and market scrapingSep 26, 2026
FP16	33K	16.1 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 16.06 GB Methodology Formula: parameterCountB × bytesPerParam. Precision: FP16 = 2 bytes/param. (parameterCountB in billions, result in GB). Assumptions & Parameters paramCountB: 8.03 quantization: fp16 bytesPerParam: 2 Refreshed daily from provider APIs and market scrapingSep 26, 2026	4.00 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 4 GB Methodology GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1. Assumptions & Parameters numLayers: 32 numKvHeads: 8 headDim: 128 contextLength: 32768 batchSize: 1 quantization: fp16 gqaRatio: 4 Refreshed daily from provider APIs and market scrapingSep 26, 2026	23.8 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 23.83 GB Methodology Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%). Assumptions & Parameters paramCountB: 8.03 quantization: fp16 contextLength: 32768 batchSize: 1 numLayers: 32 numKvHeads: 8 headDim: 128 gqaRatio: 4 architecture: llama-3.1-8b bytesPerParam: 2 bytesPerKvElement: 2 cudaOverheadGb: 1.2 activationGb: 0.4 fragmentationHeadroomPct: 10 isMla: false Refreshed daily from provider APIs and market scrapingSep 26, 2026
FP16	128K	16.1 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 16.06 GB Methodology Formula: parameterCountB × bytesPerParam. Precision: FP16 = 2 bytes/param. (parameterCountB in billions, result in GB). Assumptions & Parameters paramCountB: 8.03 quantization: fp16 bytesPerParam: 2 Refreshed daily from provider APIs and market scrapingSep 26, 2026	15.63 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 15.63 GB Methodology GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1. Assumptions & Parameters numLayers: 32 numKvHeads: 8 headDim: 128 contextLength: 128000 batchSize: 1 quantization: fp16 gqaRatio: 4 Refreshed daily from provider APIs and market scrapingSep 26, 2026	36.6 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 36.61 GB Methodology Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%). Assumptions & Parameters paramCountB: 8.03 quantization: fp16 contextLength: 128000 batchSize: 1 numLayers: 32 numKvHeads: 8 headDim: 128 gqaRatio: 4 architecture: llama-3.1-8b bytesPerParam: 2 bytesPerKvElement: 2 cudaOverheadGb: 1.2 activationGb: 0.4 fragmentationHeadroomPct: 10 isMla: false Refreshed daily from provider APIs and market scrapingSep 26, 2026
INT4	4K	4.0 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 4.01 GB Methodology Formula: parameterCountB × bytesPerParam. Precision: INT4 = 0.5 bytes/param. (parameterCountB in billions, result in GB). Assumptions & Parameters paramCountB: 8.03 quantization: int4 bytesPerParam: 0.5 Refreshed daily from provider APIs and market scrapingSep 26, 2026	0.25 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 0.25 GB Methodology GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1. Assumptions & Parameters numLayers: 32 numKvHeads: 8 headDim: 128 contextLength: 4096 batchSize: 1 quantization: int4 gqaRatio: 4 Refreshed daily from provider APIs and market scrapingSep 26, 2026	6.5 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 6.45 GB Methodology Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%). Assumptions & Parameters paramCountB: 8.03 quantization: int4 contextLength: 4096 batchSize: 1 numLayers: 32 numKvHeads: 8 headDim: 128 gqaRatio: 4 architecture: llama-3.1-8b bytesPerParam: 0.5 bytesPerKvElement: 1 cudaOverheadGb: 1.2 activationGb: 0.4 fragmentationHeadroomPct: 10 isMla: false Refreshed daily from provider APIs and market scrapingSep 26, 2026
INT4	33K	4.0 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 4.01 GB Methodology Formula: parameterCountB × bytesPerParam. Precision: INT4 = 0.5 bytes/param. (parameterCountB in billions, result in GB). Assumptions & Parameters paramCountB: 8.03 quantization: int4 bytesPerParam: 0.5 Refreshed daily from provider APIs and market scrapingSep 26, 2026	2.00 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 2 GB Methodology GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1. Assumptions & Parameters numLayers: 32 numKvHeads: 8 headDim: 128 contextLength: 32768 batchSize: 1 quantization: int4 gqaRatio: 4 Refreshed daily from provider APIs and market scrapingSep 26, 2026	8.4 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 8.38 GB Methodology Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%). Assumptions & Parameters paramCountB: 8.03 quantization: int4 contextLength: 32768 batchSize: 1 numLayers: 32 numKvHeads: 8 headDim: 128 gqaRatio: 4 architecture: llama-3.1-8b bytesPerParam: 0.5 bytesPerKvElement: 1 cudaOverheadGb: 1.2 activationGb: 0.4 fragmentationHeadroomPct: 10 isMla: false Refreshed daily from provider APIs and market scrapingSep 26, 2026
INT4	128K	4.0 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 4.01 GB Methodology Formula: parameterCountB × bytesPerParam. Precision: INT4 = 0.5 bytes/param. (parameterCountB in billions, result in GB). Assumptions & Parameters paramCountB: 8.03 quantization: int4 bytesPerParam: 0.5 Refreshed daily from provider APIs and market scrapingSep 26, 2026	7.81 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 7.81 GB Methodology GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1. Assumptions & Parameters numLayers: 32 numKvHeads: 8 headDim: 128 contextLength: 128000 batchSize: 1 quantization: int4 gqaRatio: 4 Refreshed daily from provider APIs and market scrapingSep 26, 2026	14.8 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 14.77 GB Methodology Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%). Assumptions & Parameters paramCountB: 8.03 quantization: int4 contextLength: 128000 batchSize: 1 numLayers: 32 numKvHeads: 8 headDim: 128 gqaRatio: 4 architecture: llama-3.1-8b bytesPerParam: 0.5 bytesPerKvElement: 1 cudaOverheadGb: 1.2 activationGb: 0.4 fragmentationHeadroomPct: 10 isMla: false Refreshed daily from provider APIs and market scrapingSep 26, 2026

Cloud GPUs That Fit Llama 3.1 8B Instruct

Fit = full-stack total (weights + KV at 128K) ≤ GPU VRAM. Rates are the lowest observed on-demand rows in data/providers.json, refreshed daily.

GPU	VRAM	FP16 fit	INT4 fit	Lowest on-demand
H200 SXM5	141 GB	✓ fits	✓ fits	$2.79/hr (Vast.ai)
B200 Blackwell	192 GB	✓ fits	✓ fits	$3.99/hr (Vast.ai)
H100 SXM5	80 GB	✓ fits	✓ fits	$1.89/hr (Vast.ai)
A100 80GB SXM4	80 GB	✓ fits	✓ fits	$1.59/hr (Lambda Labs)
L40S	48 GB	✓ fits	✓ fits	$0.69/hr (Vast.ai)
RTX 4090	24 GB	OOM	✓ fits	$0.34/hr (Vast.ai)

Recommended GPUs for Llama 3.1 8B Instruct

Minimum = smallest single GPU that fits the full INT4 stack (14.8 GB). Optimal = registry recommendation, falling back to the lowest observed on-demand rate among fitting cards. Every card links to its canonical GPU page.

Minimum24 GB · $0.34/hr

RTX 4090

Smallest fitting card — 24 GB vs 14.8 GB INT4 full stack.

View GPU page →

Optimal48 GB · $0.69/hr

L40S

Registry recommendation (1x L40S).

View GPU page →

Workload Scenarios Using Llama 3.1 8B Instruct

Costed workload guides that price Llama 3.1 8B Instruct against verified cloud rates and registry VRAM math.

Inference

LLM Inference

Cheapest verified cloud GPUs for serving Llama-class models: observed hourly rates, VRAM fit, and the decode-bandwidth limit that decides tokens/sec.

RAG

RAG Pipeline

What a retrieval-augmented generation stack costs per hour: embedding retrieval on shared hardware, generation leg priced on verified GPU rates.

Next steps

⚡

Open in VRAM calculatorPre-filled with Llama 3.1 8B Instruct

✓

Full model profileBenchmarks, providers & run commands

GPU

Recommended GPU1x L40S → NVIDIA L40S

Frequently Asked Questions

How much VRAM does Llama 3.1 8B Instruct need?▾

Llama 3.1 8B Instruct needs 16 GB at FP16 (weights) and 6 GB at INT4 from the param-count weight model. Full-stack totals at 128K context are 36.6 GB (FP16) and 14.8 GB (INT4) including KV-cache, CUDA overhead, and fragmentation headroom.

Can Llama 3.1 8B Instruct run on a single GPU?▾

Yes — the registry recommendation is 1x L40S, and the NVIDIA L40S (48GB GDDR6) fits the INT4 workload single-GPU. FP16 full-stack needs 36.6 GB.

What context length fits Llama 3.1 8B Instruct?▾

Registry context window is 128,000 tokens. KV-cache grows linearly with context — the quantization table shows totals at 4K, 33K, 128K context for FP16 and INT4.

Weight model: parameters × bytes-per-parameter. KV-cache: GQA formula or modeled estimate where layer geometry is unpublished.Methodology →