Llama 3.3 70B on Dual RTX 5090 workstation (2x 32 GB)
70.554B on 64 GB at 3,584 GB/s. Hardware details · Model details
Yes. Llama 3.3 70B fits on Dual RTX 5090 workstation (2x 32 GB) at the recommended
Q4_K_M configuration, requiring approximately 46.2 GB at 8K context. Practical context capacity is 32K. Expected decode for the recommended configuration is ~35.8–51.5 t/s. EstimatedCompatibilityFits
Calculated fit
Yes
Calculated Uses 80-90% of usable memory. Works, with limited headroom.
Recommended quantization
Q4_K_M
Calculated gguf
Memory required
46.2 GB
Calculated of 56.3 GB usable — 82%
Max practical context
32K
Calculated model supports 128K
Memory budget at 8K context
Model weights ⓘ
41.0 GB Calculated
KV cache ⓘ
2.5 GB Calculated
Runtime overhead ⓘ
2.6 GB Estimated
Total required
46.2 GB Calculated
Headroom ⓘ
10.2 GB Calculated
Fits with limited headroom (82% of usable memory). A longer context will not leave room for much else.
2 GPUs providing 64 GB aggregate VRAM. Assumes tensor- or layer-parallel sharding; each GPU carries its own context and communication buffers.
Multi-GPU throughput depends heavily on interconnect. Without NVLink or NVSwitch, tensor parallelism over PCIe adds latency that partially offsets the extra bandwidth.
Performance
Estimated performance · recommended Q4_K_M
Decode, prefill and TTFT below are estimates for Q4_K_M. We do not have a comparable Q4_K_M measurement on this machine.
Estimated decode
~35.8–51.5 t/s
Estimated Q4_K_M; calculated range
Estimated prefill
~710–1470 t/s
Estimated Q4_K_M; calculated range
Estimated TTFT at 8K
~5.6–11.7 s
Estimated Q4_K_M; calculated range
Hardware load reference
1180 W
Measured machine-level load; not this model run
How the estimate is calculated
- Multi-GPU: 1792 GB/s per card, with each additional card contributing 40% of its bandwidth — 2509 GB/s effective, not the 3584 GB/s aggregate. Cross-GPU collectives use PCIe rather than a dedicated GPU fabric.
- Decode: reading 70.6B active parameters at 4.85 bits/weight takes 21.53 ms at 2509 GB/s x 79% achieved efficiency.
- Prefill: 419 TFLOPS (FP16) x 2 for native FP8 tensor cores x 0.71 calibrated against measured prefill on this platform x 26% assumed model-FLOPs utilisation, divided by 2 x 70.6B parameters per token.
- Prefill throughput is highly engine-dependent. Flash attention, batch size and quantized KV all move this number substantially, and for sparse mixture-of-experts models it is the least reliable figure we produce.
- This is a calculated estimate, not a measurement. It assumes a single request, a short prompt, no speculative decoding and a warm model already resident in memory.
Estimator version estimator_v1. Stored with every estimated row so old estimates can be regenerated when the model improves.
Submit a benchmarkSubmissions stay pending until reviewed.
This model at other quantizations on this machine
| Quantization | Resident | Disk | Total needed | Utilisation | Fit | Max context |
|---|---|---|---|---|---|---|
Q4_K_Mrecommended | 41.0 GB | 41.0 GB | 46.2 GB | 82% | Fits | 32K |
Q5_K_M | 48.1 GB | 48.1 GB | 53.5 GB | 95% | Borderline | 8K |
Q8_0 | 71.2 GB | 71.2 GB | 77.2 GB | 137% | Does not fit | 0 |
BF16 | 131.4 GB | 131.4 GB | 139.3 GB | 247% | Does not fit | 0 |
Cost of running Llama 3.3 70B on this machineUnited States (federal) · C corporation · 8h/day
Monthly economic cost
$80
Calculated after tax
Codex/Claude Code
$100/mo
≈ $100/mo · local is $20 less
Net cash at purchase
$5,854
Calculated VAT not reclaimable
Total over 5 years
$4,799
Calculated after tax, after resale
Cost per USD/1M tokens
$14.47
Calculated 5.5M tokens/month
The $100 comparison uses the Codex Pro 5x / Claude Max 5x plans. A fixed planning conversion is used for USD. This compares monthly spend only: subscriptions have usage limits, local hardware has different capabilities and constraints, and taxes or regional pricing may change the charged amount. Prices checked 7 September 2026.
Purchase-price input: $5,854 in United States (federal), tax/VAT excluded · estimated · NL retail (system build) (converted catalogue estimate). This is the localized purchase input; the after-tax economic cost is calculated separately below.
Monthly breakdown
| Depreciation 4,976 over 5 years, straight-line to a 878 residual | $82.94 |
| Electricity 57.0 kWh/month at 0.140/kWh | $7.98 |
| Cost of capital 4.0%/yr on 3,366 average capital employed | $11.22 |
| Monthly cost before tax | $102.14 |
| Electricity tax shield Running costs are deductible business expenses | −$1.68 |
| First-year expensing §179 (100.0%) | −$20.49 |
| Monthly economic cost after tax | $79.98 |
Three different numbers, deliberately
Cash cost
$5,854
Money that leaves the bank account on day one, net of reclaimable VAT.
Accounting depreciation
$82.94/month
$4,976 written down over 5 years to a $878 residual.
After-tax economic cost
$79.98/month
Depreciation plus running costs plus cost of capital, less the tax those deductions save. This is the figure to compare between machines.
Assumptions and sources (verified 2026-09-07)
VAT rate
0% Official spec
VAT recoverable
0% Official spec
Federal C-corporation estimate at the flat 21% rate. Pass-through entities should use the sole-proprietor estimate as a rougher proxy.
Effective deduction rate
21.00% Calculated
Headline marginal rate 21.00%.
Depreciation
5 years, straight-line Assumption
Residual value
$878 (15%) Assumption
Two GPU generations later, the card is worth a fraction of its list price.
Electricity
$0.140/kWh Assumption
Average power draw
324 W Calculated
Load 1180 W for 20% of powered hours, idle 110 W for the rest — a machine that is on is not generating tokens the whole time.
Investment allowances
§179 100.0% Official spec
Worth $1,229 in total — first-year expensing that replaces later tax depreciation.
Cost of capital
$11.22/month Assumption
- VAT rate: No US federal VAT · verified 2026-09-07
- Marginal tax rate: IRS Publication 542 — Corporations · verified 2026-09-07
- VAT recoverable fraction: site assumption · verified 2026-09-07
- Useful life: IRS Publication 946 — How To Depreciate Property · verified 2026-09-07
- Electricity price: Site assumption · verified 2026-09-07
Assumes the machine generates tokens 20% of its 176 powered hours per month. Cost per token scales inversely with this number — halve the utilisation and the cost per token doubles.
Federal planning estimate only, not tax advice. State and local income tax, sales/use tax and incentives are excluded. It assumes 100% business use, a Section 179 election, enough business income to use it, and that the full annual limit remains available.
Efficiency metrics
Decode per $1,000 spent
7.4 t/s
Calculated purchase price only
Decode per $100/month
54.5 t/s
Calculated after-tax ownership cost
Tokens per joule
0.13
Calculated same as tokens/s per watt
USD per 1M output tokens
$14.47
Calculated at 20% utilisation
Local versus hosted APIs
| Hosted model | USD/1M output | Break-even | API at your volume | Verdict | Comparison type |
|---|---|---|---|---|---|
| Qwen3.8 Flash Alibaba Cloud | $0.42 | 51.9M/mo | $9 | API cheaper | Different model |
| DeepSeek-V4.1 Flash DeepSeek | $0.60 | 44.4M/mo | $10 | API cheaper | Different model |
| DeepSeek-V4 Pro DeepSeek | $1.98 | 11.0M/mo | $40 | API cheaper | Different model |
| DeepSeek V4 Pro (Together) Together AI | $4.40 | 3.8M/mo | $117 | Local cheaper | Different model |
| GLM-5.3 Z.ai | $4.40 | 5.1M/mo | $86 | About equal | Different model |
| Claude Haiku 4.5 Anthropic | $5.00 | 6.2M/mo | $72 | API cheaper | Different model |
Different-model comparison: the hosted model is not the model you would run locally. Treat this as a workload-quality trade-off, not a direct economic equivalence — the frontier model may complete a task in fewer tokens, or complete tasks the local model cannot. Break-even is the monthly output volume at which API spend equals the $80/month economic cost of owning this machine, assuming 8 input tokens per output token. Hosted prices are published in USD. This table is the canonical US default.
Llama 3.3 70B on other hardware
| Hardware | Decode | Price | Fit |
|---|---|---|---|
| NVIDIA DGX H200 (8x H200, 1,128 GB) | ~198–285 t/s | $359,430 * | Comfortable |
| Quad RTX 5090 workstation (4x 32 GB) | ~32.3–46.4 t/s | $12,611 * | Comfortable |
| Lenovo ThinkStation PX (4x RTX PRO 6000, 384 GB) | ~27.9–40.2 t/s | $58,553 * | Comfortable |
| RTX PRO 6000 Blackwell workstation (96 GB) | ~21.3–30.7 t/s | $10,359 * | Comfortable |
| RTX PRO 6000 Max-Q workstation (96 GB, 300 W) | ~21.3–30.7 t/s | $10,809 * | Comfortable |
| Dell Precision 7960 Rack (2x RTX PRO 6000, 192 GB) | ~19.1–27.5 t/s | $36,663 * | Comfortable |
Nearest alternatives to the Dual RTX 5090 workstation (2x 32 GB)
Llama 3.3 70B on RTX 5090 workstation (1x 32 GB)32 GBLlama 3.3 70B on Quad RTX 5090 workstation (4x 32 GB)128 GBLlama 3.3 70B on Dual used RTX 4090 workstation (48 GB)48 GBLlama 3.3 70B on RTX PRO 6000 Blackwell workstation (96 GB)96 GBLlama 3.3 70B on RTX PRO 6000 Max-Q workstation (96 GB, 300 W)96 GB
Compare Dual RTX 5090 workstation (2x 32 GB) against RTX 5090 workstation (1x 32 GB) →
Compare Dual RTX 5090 workstation (2x 32 GB) against RTX 5090 workstation (1x 32 GB) →