Llama 3.3 70B Instruct VRAM Requirements: FP16 / INT4 / KV-Cache (2026)
Llama 3.3 70B Instruct runs on 70.6B 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.
140 GB FP16 weights
weights = parametersB × bytes-per-parameter (FP16 = 2 B, INT4 = 0.5 B)
40 GB INT4 weights
weights = parametersB × bytes-per-parameter (FP16 = 2 B, INT4 = 0.5 B)
128K context window
How much VRAM does Llama 3.3 70B Instruct need?
Weights: 140 GB at FP16, 40 GB at INT4 (parameters × bytes-per-parameter: FP16 = 2 B, INT4 = 0.5 B).
Full stack at 128K: 172.1 GB FP16 / 48.1 GB INT4 — including KV-cache, CUDA overhead, activations, and fragmentation headroom from the canonical VRAM engine.
Single-GPU verdict: INT4 fits 80 GB datacenter cards (A100/H100); FP16 needs 240 GB → multi-GPU.
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 | 141.2 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 141.2 GB Methodology Formula: parameterCountB × bytesPerParam. Precision: FP16 = 2 bytes/param. (parameterCountB in billions, result in GB). Assumptions & Parameters
Refreshed daily from provider APIs and market scrapingSep 26, 2026 | 0.44 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 0.44 GB Methodology GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1. Assumptions & Parameters
Refreshed daily from provider APIs and market scrapingSep 26, 2026 | 157.6 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 157.56 GB Methodology Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%). Assumptions & Parameters
Refreshed daily from provider APIs and market scrapingSep 26, 2026 |
| FP16 | 33K | 141.2 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 141.2 GB Methodology Formula: parameterCountB × bytesPerParam. Precision: FP16 = 2 bytes/param. (parameterCountB in billions, result in GB). Assumptions & Parameters
Refreshed daily from provider APIs and market scrapingSep 26, 2026 | 3.50 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 3.5 GB Methodology GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1. Assumptions & Parameters
Refreshed daily from provider APIs and market scrapingSep 26, 2026 | 160.9 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 160.93 GB Methodology Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%). Assumptions & Parameters
Refreshed daily from provider APIs and market scrapingSep 26, 2026 |
| FP16 | 128K | 141.2 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 141.2 GB Methodology Formula: parameterCountB × bytesPerParam. Precision: FP16 = 2 bytes/param. (parameterCountB in billions, result in GB). Assumptions & Parameters
Refreshed daily from provider APIs and market scrapingSep 26, 2026 | 13.67 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 13.67 GB Methodology GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1. Assumptions & Parameters
Refreshed daily from provider APIs and market scrapingSep 26, 2026 | 172.1 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 172.12 GB Methodology Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%). Assumptions & Parameters
Refreshed daily from provider APIs and market scrapingSep 26, 2026 |
| INT4 | 4K | 35.3 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 35.3 GB Methodology Formula: parameterCountB × bytesPerParam. Precision: INT4 = 0.5 bytes/param. (parameterCountB in billions, result in GB). Assumptions & Parameters
Refreshed daily from provider APIs and market scrapingSep 26, 2026 | 0.22 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 0.22 GB Methodology GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1. Assumptions & Parameters
Refreshed daily from provider APIs and market scrapingSep 26, 2026 | 40.8 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 40.83 GB Methodology Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%). Assumptions & Parameters
Refreshed daily from provider APIs and market scrapingSep 26, 2026 |
| INT4 | 33K | 35.3 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 35.3 GB Methodology Formula: parameterCountB × bytesPerParam. Precision: INT4 = 0.5 bytes/param. (parameterCountB in billions, result in GB). Assumptions & Parameters
Refreshed daily from provider APIs and market scrapingSep 26, 2026 | 1.75 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 1.75 GB Methodology GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1. Assumptions & Parameters
Refreshed daily from provider APIs and market scrapingSep 26, 2026 | 42.5 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 42.52 GB Methodology Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%). Assumptions & Parameters
Refreshed daily from provider APIs and market scrapingSep 26, 2026 |
| INT4 | 128K | 35.3 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 35.3 GB Methodology Formula: parameterCountB × bytesPerParam. Precision: INT4 = 0.5 bytes/param. (parameterCountB in billions, result in GB). Assumptions & Parameters
Refreshed daily from provider APIs and market scrapingSep 26, 2026 | 6.84 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 6.84 GB Methodology GQA KV Cache: kvBytes = 2 × numLayers × numKvHeads × headDim × contextLength × batchSize × bytesPerKvElement. GQA ratio: 4:1. Assumptions & Parameters
Refreshed daily from provider APIs and market scrapingSep 26, 2026 | 48.1 GB Calculated EstimateHIGH SourceDeterministic VRAM Canonical Engine VerifiedSep 26, 2026 Value 48.11 GB Methodology Total = weights + kvCache + cudaOverhead + activation + fragmentationHeadroom (10%). Assumptions & Parameters
Refreshed daily from provider APIs and market scrapingSep 26, 2026 |
Cloud GPUs That Fit Llama 3.3 70B 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 | OOM | ✓ fits | $2.79/hr (Vast.ai) |
| B200 Blackwell | 192 GB | ✓ fits | ✓ fits | $3.99/hr (Vast.ai) |
| H100 SXM5 | 80 GB | OOM | ✓ fits | $1.89/hr (Vast.ai) |
| A100 80GB SXM4 | 80 GB | OOM | ✓ fits | $1.59/hr (Lambda Labs) |
| L40S | 48 GB | OOM | OOM | $0.69/hr (Vast.ai) |
| RTX 4090 | 24 GB | OOM | OOM | $0.34/hr (Vast.ai) |
Recommended GPUs for Llama 3.3 70B Instruct
Minimum = smallest single GPU that fits the full INT4 stack (48.1 GB). Optimal = registry recommendation, falling back to the lowest observed on-demand rate among fitting cards. Every card links to its canonical GPU page.
Workload Scenarios Using Llama 3.3 70B Instruct
Costed workload guides that price Llama 3.3 70B Instruct against verified cloud rates and registry VRAM math.
Cheapest verified cloud GPUs for serving Llama-class models: observed hourly rates, VRAM fit, and the decode-bandwidth limit that decides tokens/sec.
Fine-TuningVerified hourly rates for LoRA and QLoRA fine-tuning: which GPUs fit 32B-70B adapters, and where optimizer state beats weights.
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