Recommended Free Tools
Estimate an LLM’s weight memory first, then budget separately for its KV cache, activations, and other runtime allocations. To estimate inference cost, combine the current price of the deployment with throughput measured on the model and workload you actually plan to serve. A parameter-count calculation is a useful starting point—not proof that a model will fit or a prediction of its cost per token.
How to estimate the model’s GPU memory
For a first-pass estimate of weight memory on each GPU, use:
Estimated weight memory per GPU = parameter count × bytes per parameter ÷ tensor-parallel degree
Use the precision or quantization format actually used by the checkpoint and runtime. NVIDIA’s versioned NIM 2.0.13 guide, accessed in 2026, gives these bytes-per-parameter estimates:
#1 Best Overall
- Read Before You Buy — No Video Output: These adapters support charging and USB 2.0 data transfer, but cannot transmit video signals. Except for standard USB webcams (which use USB data only), they are not compatible with HDMI/DisplayPort cables, video-capable USB-C hubs, or docking stations with video output.
- Convert USB-A Ports to USB-C: Designed to connect USB-C earphones, cables, flash drives, card readers, and other USB-C accessories to standard USB-A ports. Plug-and-play with no drivers or software required.
- Aluminum Alloy Housing: Built with a sturdy aluminum alloy shell that aids in heat dissipation and protects against daily wear and scratches. Designed to maintain a stable and secure connection.
- Compact & Travel-Friendly: The ultra-compact design allows the adapter to stay plugged into your device without blocking adjacent ports or adding bulk, reducing wear and tear on your original USB ports.
- 12-Month Warranty: Backed by a 12-month manufacturer warranty for peace of mind. Designed to meet strict quality control standards for reliable everyday performance.
| Weight format | Estimated bytes per parameter |
|---|---|
| BF16 or FP16 | 2 |
| FP8 | 1 |
| INT4 or NVFP4 | 0.5 |
These are planning figures, not an exact prediction of checkpoint size or allocated VRAM. Metadata, quantization scales, unquantized layers, and packing can make the real memory use differ from simple multiplication. Sharding also depends on the actual parallelism and implementation; dividing evenly by tensor-parallel degree is an initial estimate, not a guarantee that every component will be distributed equally.
Worked weight-memory estimates
| Model and format | Calculation | Estimated weight memory | Source and qualification |
|---|---|---|---|
| Llama 3.1 8B, BF16, TP=1 | 8 billion × 2 bytes ÷ 1 | 16 GB total | NVIDIA NIM 2.0.13 sizing estimate, accessed in 2026 |
| Llama 3.3 70B, BF16, TP=4 | 70 billion × 2 bytes ÷ 4 | 35 GB per GPU | NVIDIA NIM 2.0.13 sizing estimate, accessed in 2026 |
| Llama 3.3 70B, FP8, TP=2 | 70 billion × 1 byte ÷ 2 | 35 GB per GPU | NVIDIA NIM 2.0.13 sizing estimate, accessed in 2026 |
NVIDIA’s guide uses a 24 GB GPU, including an RTX 4090, as an example that can hold the estimated 16 GB of weights for Llama 3.1 8B in BF16, leaving capacity for cache and overhead. That is an illustrative weight estimate, not a guarantee for a particular context length, concurrency level, model revision, or runtime.
Keep units in view when matching estimates to hardware. Vendor figures may use decimal GB while tools report GiB. Do not size a GPU exactly to a rounded weights-only estimate and assume the remaining capacity will cover runtime needs.
Rank #2
- 5-in-1 USB-C Hub: Experience comprehensive connectivity featuring a Power Delivery input, two USB-A 2.0 ports, a USB-A 3.0 port, and an HDMI port. (Note: The USB-C power delivery input port is only for connecting an external wall charger to power your laptop and cannot power peripheral devices.)
- 90W Pass-Through Charging: Achieve optimal charging with 90W pass-through power to your laptop, supported by a total input of 100W, with the hub reserving 10W for operational efficiency. (Note: Wall charger not included.)
- Quick Data Transfers: Accelerate your productivity with rapid data transfers using a high-speed 5Gbps USB 3.0 port and two 480Mbps USB 2.0 ports.
- 4K HDMI Display: Enhance your visual experience with a hub capable of delivering 4K resolution at 30Hz in both mirror and extend modes. Please note that this hub is compatible with MacBook (macOS 12 and newer), Windows 10 and 11, ChromeOS, and laptops equipped with DP Alt Mode and Power Delivery. Note: This device is not compatible with Linux.
- What You Get: Anker USB-C Hub (5-in-1, 4K HDMI), welcome guide, 18-month warranty, and our friendly customer service.
What else uses memory during inference?
Weights are only one part of the runtime budget. TensorRT-LLM identifies weights, activations, and I/O tensors—especially the KV cache—as major memory contributors. NVIDIA’s NIM guidance also calls out communication buffers, CUDA graphs, adapters, and multimodal reservations where applicable. The allocation order and accounting can vary by backend version and model.
Free tools Windows power users keep installed
One-click scans. No signup required.
- KV cache: Stores keys and values for prior tokens so they do not need to be recomputed. It grows with tokens in use; longer contexts and more simultaneous sequences increase its demand.
- Activations: Intermediate tensors used during inference. Their peak memory depends on the workload and engine configuration.
- Communication and runtime buffers: Additional allocations associated with parallel execution and the serving engine.
- Graph capture and model-specific reservations: CUDA graphs, adapters, or multimodal state can add memory requirements when used.
There is no reliable universal KV-cache figure that can be derived from parameter count alone. Cache demand depends on architecture and attention structure, cache precision, context, concurrency, and serving-engine behavior. A model may load successfully yet fail when a request asks for a context or cache allocation the available memory cannot support.
Estimate memory for your actual workload
- Identify the exact model and checkpoint. Record its parameter count, architecture, revision, and checkpoint metadata. A family name alone does not establish the exact memory requirement.
- Confirm the deployed weight format. Apply the precision or quantization used by the checkpoint and runtime, rather than assuming the format from the model name.
- Map the real device topology. Use tensor-parallel degree as a first-pass divisor for weights, then check how the selected implementation distributes memory across devices.
- Set workload limits. Specify maximum input or context length, output length, batch size or concurrency, and latency target. In TensorRT-LLM, activation memory depends on maximum shapes and build-time limits such as batch and token counts, so configured maxima can use capacity even when typical requests are smaller.
- Measure the runtime allocation. Inspect startup logs and allocator measurements for the selected model, engine, version, and configuration. Include cache, peak activations, buffers, graph capture, and applicable adapters or multimodal state.
- Leave headroom and validate under load. Treat the engine’s memory-utilization setting as a budget control, not extra physical memory. vLLM warns that increasing its reservation can increase KV-cache capacity but can also cause an out-of-memory failure.
Compare the measured allocation with usable GPU memory under the same setup and representative request mix. An estimate helps narrow hardware choices; only validation on the intended engine and configuration can establish whether the target workload fits.
Rank #3
- Sleek 7-in-1 USB-C Hub: Features an HDMI port, two USB-A 3.0 ports, and a USB-C data port, each providing 5Gbps transfer speeds. It also includes a USB-C PD input port for charging up to 100W and dual SD and TF card slots, all in a compact design.
- Flawless 4K@60Hz Video with HDMI: Delivers exceptional clarity and smoothness with its 4K@60Hz HDMI port, making it ideal for high-definition presentations and entertainment. (Note: Only the HDMI port supports video projection; the USB-C port is for data transfer only.)
- Double Up on Efficiency: The two USB-A 3.0 ports and a USB-C port support a fast 5Gbps data rate, significantly boosting your transfer speeds and improving productivity.
- Fast and Reliable 85W Charging: Offers high-capacity, speedy charging for laptops up to 85W, so you spend less time tethered to an outlet and more time being productive.
- What You Get: Anker USB-C Hub (7-in-1), welcome guide, 18-month warranty, and our friendly customer service.
How to estimate inference cost per token
First distinguish infrastructure billed over time from services billed by token. For a self-hosted or rented GPU, a basic measured output-token calculation is:
Cost per generated token = compute charges during the measurement interval ÷ generated output tokens during that interval
What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
Dollars per million generated tokens = cost per generated token × 1,000,000
Rank #4
- Dual Converters, Infinite Potential：Includes 2× USB C male to USB A female adapters and 2× USB A male to USB C female adapters. Perfect for a wide range of uses—tablets with Bluetooth keyboards, expand USB ports on macbook, and more. Two different converters for all your daily needs
- Next-Level 10Gbps & 3A Charging: No more slow 480Mbps, this usb to usb c adapter has a transfer speed of up to 10Gbps, allowing you to do more transferring in less time. This usb adapter fits both USB A and USB C charger, supporting up to 3A fast charging
- Upgraded Exquisite Craftsmanship: With an aluminum alloy housing and metal connector, the usbc to usb adapter is extremely durable and sturdy. Rigorously tested to withstand more than 10,000 times of plugging and unplugging, ensuring long-lasting performance
- Broad Compatible: The usb c to usb adapter widely supports all USB C/ USB A devices like laptops, tablets, cellphones, car chargers, and phone chargers. Such as compatible with MacBook Pro/Air 2023/2022, Thunderbolt 4/3 Devices,Apple MagSafe Watch 9/8/7/SE/Ultra, iPad Pro 2022/2021, Samsung Galaxy S23/S20/S10, and iPhone 17/16/15 Pro. Plug and play
- Please Note: To reach 10Gbps speed, keep the cable under 3.3 ft. For USB A Male to USB C adapters, try flipping the USB C connector. USB C Male to USB A adapters support bidirectional 10Gbps transfer within 3.3 ft
Measure the charges and output tokens over the same interval. State what the charges include—such as GPU instance, CPU, RAM, storage, network, idle time, replicas, discounts, and operational overhead—so the result is interpretable. If input processing matters to the workload, report input and output token volumes separately rather than blending them into one rate.
Choose the calculation that matches the billing model
| Deployment model | What to use | What the result depends on |
|---|---|---|
| Self-hosted or rented GPU | Compute charges over a measured interval divided by output tokens generated in that interval | Billable instance or GPU rate, billing interval, useful throughput, utilization, and which infrastructure costs are included |
| Managed endpoint billed over time | Provider’s published rate × duration × number of replicas, when that is the provider’s documented method | Current rate, replica count, duration, region, and billing terms. Hugging Face documents this rate-times-duration-times-replicas approach and says its displayed hourly endpoint rates are billed per minute. |
| Dedicated inference billed by GPU-hour | GPU-hours charged, combined with measured workload throughput for a cost-per-token estimate | Current provider price and the output volume served during billable GPU time. DigitalOcean describes dedicated inference billing per GPU-hour. |
| Managed API billed by tokens | Current provider rate applied to the actual billed input and output token counts | Provider, model, billing unit, input/output mix, and the applicable current rate |
These are examples of pricing models, not universal billing terms. Use the provider’s current published price and billing unit for the deployment under consideration.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Benchmark before comparing deployments
An hourly GPU price does not determine cost per token by itself. Measure useful throughput on the intended model, precision, serving engine, prompt and output lengths, concurrency, and latency target. Then calculate cost using the charges and token counts from that representative run. A faster configuration may not be cheaper if it costs more per hour, and a low hourly price may not be economical if utilization or throughput is poor.
Best Value
- 5-in-1 Connectivity: Equipped with a 4K HDMI port, a 5 Gbps USB-C data port, two 5 Gbps USB-A ports, and a USB C 100W PD-IN port. Note: The USB C 100W PD-IN port supports only charging and does not support data transfer devices such as headphones or speakers.
- Powerful Pass-Through Charging: Supports up to 85W pass-through charging so you can power up your laptop while you use the hub. Note: Pass-through charging requires a charger (not included). Note: To achieve full power for iPad, we recommend using a 45W wall charger.
- Transfer Files in Seconds: Move files to and from your laptop at speeds of up to 5 Gbps via the USB-C and USB-A data ports. Note: The USB C 5Gbps Data port does not support video output.
- HD Display: Connect to the HDMI port to stream or mirror content to an external monitor in resolutions of up to 4K@30Hz. Note: The USB-C ports do not support video output.
- What You Get: Anker 332 USB-C Hub (5-in-1), welcome guide, our worry-free 18-month warranty, and friendly customer service.
NVIDIA’s 2024 LLM Inference Sizing presentation says, in the context it evaluated, that “The cost and the latency are usually dominated by the number of output tokens.” Treat that as context-specific rather than a universal rule: long prompts, low utilization, strict time-to-first-token or inter-token latency targets, batching, and concurrency can change the economics. Latency constraints can also lower throughput.
For a fair comparison, hold the model revision and quality level constant, and evaluate:
- Usable VRAM against the combined weights, cache, activation, and runtime budget.
- Weight and KV-cache precision, including any quality impact that must be evaluated for the task.
- Maximum context and concurrent requests at the required latency.
- Measured input and output throughput under the chosen batching and scheduling configuration.
- Cost per request or per million input and output tokens at realistic utilization.
- Region, availability, billing granularity, commitment or interruptibility, and additional instance charges.
Cloud pricing and availability can change. AWS says Capacity Blocks rates are updated with supply and demand. When publishing or planning a cost figure, identify the provider, region, instance configuration, GPU count, operating system, reservation, spot, or on-demand billing type, and verification date.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
Quick wins for a faster PC:
Repair Windows errors before they cause bigger problemsFix Now →Scan for outdated or missing drivers - takes under a minuteDriver Scan →
