Phi-4 14B on Used RTX 4090 workstation (24 GB)
14.66B on 24 GB at 1,008 GB/s. Hardware details · Model details
CompatibilityComfortable
Fits
Yes
Calculated Uses under 80% of usable memory. Room for a long context and other work.
Recommended quantization
Q4_K_M
Calculated gguf
Memory required
11.3 GB
Calculated of 22.1 GB usable — 51%
Max practical context
16K
Calculated model supports 16K
Memory budget at 8K context
Model weights ⓘ
8.5 GB Calculated
KV cache ⓘ
1.6 GB Calculated
Runtime overhead ⓘ
1.3 GB Estimated
Total required
11.3 GB Calculated
Usable memory ⓘ
22.1 GB Assumption
Headroom
10.7 GB Calculated
Highest-precision quantization that leaves headroom: uses 51% of usable memory at 8K context.
Discrete GPU: 24 GB of VRAM, of which we assume 92% is usable after driver and context overhead.
PerformanceEstimated0/10
Decode (generation)
~69.4–99.8 t/s
Estimated calculated, not measured
Prefill (prompt)
~2340–4860 t/s
Estimated
TTFT at 8K
~1.7–3.6 s
Estimated time to first token
Power while generating
520 W
Measured 0.56 tokens/s per watt
These figures are estimated, not measured. Nobody has published a benchmark of Phi-4 14B on this machine that we know of, so we calculate throughput from memory bandwidth and active parameter count. Estimates are always shown as a range. Expand the working below to see exactly how the number was produced.
How the estimate is calculated
- Decode: reading 14.7B active parameters at 4.85 bits/weight takes 11.02 ms at 1008 GB/s x 80% achieved efficiency.
- Prefill: 165 TFLOPS (FP16) x 2 for native FP8 tensor cores x 32% assumed model-FLOPs utilisation, divided by 2 x 14.7B 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.
This model at other quantizations on this machine
| Quantization | Weights | Total needed | Utilisation | Fit | Max context |
|---|---|---|---|---|---|
MLX 4-bit | 8.0 GB | 10.8 GB | 49% | Comfortable | 16K |
Q4_K_Mrecommended | 8.5 GB | 11.3 GB | 51% | Comfortable | 16K |
Q8_0 | 14.8 GB | 17.8 GB | 81% | Fits | 16K |
BF16 | 27.3 GB | 30.7 GB | 139% | Does not fit | 0 |
Cost of running Phi-4 14B on this machineUnited States (federal) · C corporation · 8h/day
Monthly economic cost
$27
Calculated after tax
Codex/Claude Code
$100/mo
≈ $100/mo · local is $73 less
Net cash at purchase
$2,071
Calculated VAT not reclaimable
Total over 5 years
$1,611
Calculated after tax, after resale
Cost per USD/1M tokens
$2.50
Calculated 10.7M 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.
Monthly breakdown
| Depreciation 1,615 over 5 years, straight-line to a 456 residual | $26.92 |
| Electricity 26.8 kWh/month at 0.140/kWh | $3.75 |
| Cost of capital 4.0%/yr on 1,263 average capital employed | $4.21 |
| Monthly cost before tax | $34.88 |
| Electricity tax shield Running costs are deductible business expenses | −$0.79 |
| First-year expensing §179 (100.0%) | −$7.25 |
| Monthly economic cost after tax | $26.84 |
Three different numbers, deliberately
Cash cost
$2,071
Money that leaves the bank account on day one, net of reclaimable VAT.
Accounting depreciation
$26.92/month
$1,615 written down over 5 years to a $456 residual.
After-tax economic cost
$26.84/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
$456 (22%) Assumption
Extrapolated.
Electricity
$0.140/kWh Assumption
Average power draw
152 W Calculated
Load 520 W for 20% of powered hours, idle 60 W for the rest — a machine that is on is not generating tokens the whole time.
Investment allowances
§179 100.0% Official spec
Worth $435 in total — first-year expensing that replaces later tax depreciation.
Cost of capital
$4.21/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
40.8 t/s
Calculated purchase price only
Decode per $100/month
315.1 t/s
Calculated after-tax ownership cost
Tokens per joule
0.56
Calculated same as tokens/s per watt
USD per 1M output tokens
$2.50
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 | 17.4M/mo | $17 | API cheaper | Different model |
| DeepSeek-V4 Flash DeepSeek | $0.66 | 11.1M/mo | $26 | About equal | Different model |
| DeepSeek-V4 Pro DeepSeek | $1.98 | 3.7M/mo | $78 | Local cheaper | Different model |
| DeepSeek V4 Pro (Together) Together AI | $4.40 | 1.3M/mo | $227 | Local cheaper | Different model |
| GLM-5.3 Z.ai | $4.40 | 1.7M/mo | $167 | Local cheaper | Different model |
| Claude Haiku 4.5 Anthropic | $5.00 | 2.1M/mo | $139 | Local 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 $27/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.
Phi-4 14B on other hardware
| Hardware | Decode | Price | Fit |
|---|---|---|---|
| Quad RTX 5090 workstation (4x 32 GB) | ~128–185 t/s | €13.999 | Comfortable |
| Dual RTX 5090 workstation (2x 32 GB) | ~88.7–128 t/s | €6.499 | Comfortable |
| RTX 5090 workstation (1x 32 GB) | ~69.6–100 t/s | €3.699 | Comfortable |
| RTX PRO 6000 Blackwell workstation (96 GB) | ~65.6–94.4 t/s | €11.499 | Comfortable |
| RTX PRO 6000 Max-Q workstation (96 GB, 300 W) | ~65.6–94.4 t/s | €11.999 | Comfortable |
| Dual used RTX 4090 workstation (48 GB) | ~52.7–75.9 t/s | €4.099 | Comfortable |
Nearest alternatives to the Used RTX 4090 workstation (24 GB)
Phi-4 14B on Dual used RTX 4090 workstation (48 GB)48 GBPhi-4 14B on RTX 5090 workstation (1x 32 GB)32 GBPhi-4 14B on Radeon AI PRO R9700 workstation (32 GB)32 GBPhi-4 14B on Dual RTX 5090 workstation (2x 32 GB)64 GBPhi-4 14B on RTX PRO 6000 Blackwell workstation (96 GB)96 GB
Compare Used RTX 4090 workstation (24 GB) against Dual used RTX 4090 workstation (48 GB) →
Compare Used RTX 4090 workstation (24 GB) against Dual used RTX 4090 workstation (48 GB) →
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