An “80B-active” label is not enough to determine VRAM, GPU count, or cost. In a mixture-of-experts (MoE) model, the active-parameter figure describes the experts used for each token, while memory depends mainly on the model’s total parameters, weight precision, KV-cache geometry, context length, concurrency, and serving runtime. Obtain those values from the model card, calculate a memory range, then benchmark the exact checkpoint before converting throughput into a per-token price.
The worked 160B-total-parameter examples below are illustrations, not a claim about every 80B-active model. Replace 160B with the actual checkpoint total.
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Why 80B active parameters do not tell you the VRAM requirement
MoE routing selects only a subset of experts for each token, reducing per-token arithmetic compared with a dense model that stores and evaluates every parameter. It does not normally eliminate the need to keep the inactive experts available. NVIDIA summarizes the distinction as: “MoE decouples memory (total params → VRAM to host) from compute (active params → FLOPs per token).”
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Collect the inputs before doing arithmetic
Model-card inputs
- Total parameter count, including all experts.
- Active parameter count (80B for the model in this guide).
- Number of transformer or recurrent layers.
- Number of key-value (KV) heads and the attention head dimension.
- Maximum context and the context length your product will actually allow.
- Checkpoint weight precision and any per-component differences. Experts, embeddings, and output heads may not all use the same format.
- Whether the architecture is hybrid or recurrent. A conventional KV-cache formula may not apply identically to every layer.
Workload and service-level inputs
- Average and peak input tokens per request.
- Average and peak generated tokens.
- Concurrent sequences and requests per second.
- Time-to-first-token (TTFT), end-to-end latency, tail-latency, and availability targets.
- Whether prefix caching, continuous batching, or streaming is enabled.
Commercial inputs
- The complete instance or GPU-fleet hourly price in the region you will use.
- Host CPU and RAM, local or network storage, networking, orchestration, monitoring, and the number of replicas required for availability.
- Expected average and peak utilization. An hourly price divided by peak throughput overstates efficiency when traffic is intermittent.
Step 1: estimate resident weight memory
The first estimate is raw checkpoint storage:
weight_bytes ≈ total_parameters × (weight_bits / 8)
AWS planning guidance uses these approximate bytes-per-parameter values:
| Weight format | Approximate bytes per parameter | Typical planning interpretation |
|---|---|---|
| FP16 or BF16 | 2 | Highest weight memory among the formats listed; often selected when quality and kernel support take priority. |
| FP8 or INT8 | 1 | About half the raw weight memory of FP16/BF16, subject to hardware and kernel support. |
| INT4 or NVFP4 | 0.5 | About one quarter of FP16/BF16 raw weight memory; quality and implementation trade-offs must be measured. |
Convert bytes to GB or GiB consistently. The raw result is not the amount of HBM available to requests: CUDA allocations, framework metadata, activations, communication buffers, the KV-cache pool, and allocator fragmentation need headroom.
Illustrative 160B-total-parameter calculation
Because an 80B-active label does not specify total parameters, consider a clearly labeled 160B-total scenario:
| Weight format | Calculation | Raw weight memory |
|---|---|---|
| FP16/BF16 | 160B × 2 bytes | Approximately 320 GB |
| FP8/INT8 | 160B × 1 byte | Approximately 160 GB |
| INT4 | 160B × 0.5 byte | Approximately 80 GB |
These figures describe only resident weights. They do not mean that every 80B-active model has 160B total parameters, nor that a device with the same advertised HBM can load the raw number without a safety margin.
Step 2: size the KV cache for the real workload
At long context or high concurrency, the KV cache can consume more memory than expected. AWS gives this planning formula:
KV_cache_bytes = 2 × kv_bytes × num_layers × num_kv_heads × head_dim × context_length × batch_size
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Why context and concurrency change the answer
Doubling context length doubles KV memory. Four simultaneous sequences require roughly four times the KV memory of one sequence at the same length. Layer count, KV-head count, and head dimension are architectural properties that cannot be inferred safely from the active-parameter label.
AWS’s worked Mistral-7B example illustrates the scale change: one request at 1,000 tokens uses about 0.12 GB of KV cache, while four concurrent requests at 16,000 tokens use about 7.81 GB. Those are model-specific example values, not a prediction for an 80B-active model, but they show why a single short prompt can produce a misleading memory result.
Hybrid and recurrent architectures
Some models combine attention with recurrent or state-space components. Verify the model documentation before applying a standard transformer KV formula to every layer. The persistent state may have different dimensions, and only the attention layers may create conventional key/value tensors.
Step 3: add activations, runtime memory, and safety margin
AWS states that “Inference memory consumption is primarily driven by three components”: model weights and activations, the KV cache, and runtime overhead. For a deployment estimate, express the total as:
required_memory = weights + KV_cache + activations + runtime/workspace + safety_margin
- Activations: temporary tensors created during prefill and decode. Large batches and long prompts increase the peak.
- Runtime and workspace: CUDA allocations, attention and quantization kernels, graph captures, communication buffers, tokenizer workers, and framework metadata.
- Safety margin: space for allocator fragmentation, request-length variation, rolling deploys, and operational spikes.
Do not fill advertised HBM to the last gigabyte. Leave enough room for the serving engine and a realistic KV-cache pool, then verify the result under the exact runtime and checkpoint format.
Step 4: translate memory into a GPU topology
Compare the required memory with usable HBM per accelerator, not the headline capacity. AWS’s current prescriptive figures list:
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| Accelerator | Advertised HBM |
|---|---|
| H100 | 80 GB |
| H200 | 141 GB |
| B200 | 180 GB |
| B300 | 268 GB |
For the 160B-total example, raw FP16/BF16 weights alone are about 320 GB. That exceeds one H100, H200, or B200 before any cache or runtime allocation; a multi-GPU design is required. Raw FP8/INT8 weights are about 160 GB, still above one H100 and leaving little or no room on a single 180-GB device once overhead is included. Raw INT4 weights are about 80 GB, which matches an H100’s headline capacity but leaves no practical space for KV cache, activations, and runtime allocations. These are lower-bound observations, not guaranteed production GPU counts.
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Use tensor parallelism, expert parallelism, pipeline parallelism, or a combination to distribute the model. Google documents tensor parallelism for splitting a model across GPUs when one device cannot hold it. The choice affects communication, implementation complexity, and latency. Fast interconnects can make a topology viable; a slower link can erase the apparent throughput benefit.
Minimum-count arithmetic versus a deployable count
Divide raw weight memory by HBM to obtain a theoretical lower bound, then add cache and overhead. For example, 320 GB divided by 80 GB is four H100s before overhead. That does not establish that a four-H100 server will run the model at the desired context, batch size, or latency. A usable design may need an additional device or a lower-memory format, and only a benchmark can settle the trade-off.
Why active parameters still matter after memory sizing
The 80B-active figure helps estimate arithmetic per token and explains why an MoE can process tokens more efficiently than a dense model with a similar total parameter count. It is useful when comparing expected compute pressure, prefill work, and decode throughput.
It does not make inactive experts free. Routing, all-to-all communication, weight movement, and synchronization can narrow the throughput advantage at high concurrency. Measured output tokens per second on the chosen engine is more reliable than a FLOPs-only estimate.
Use a benchmark to validate fit, latency, and throughput
Memory fit is a gate, not a performance result. Benchmark the exact checkpoint, quantization, engine, GPU topology, context distribution, and concurrency that you intend to deploy.
- Load the production checkpoint and tokenizer, including the same expert layout and precision.
- Test representative prompt lengths and generated lengths, including peak cases.
- Run the expected concurrency and requests-per-second mix with continuous or static batching as appropriate.
- Record prefill throughput, decode tokens per second, TTFT, median latency, p95/p99 tail latency, GPU memory utilization, and request errors.
- Repeat after enabling prefix caching, KV quantization, or parallelism changes; each can alter both memory and latency.
- Confirm that a long-duration run does not leak memory or fail when the allocator reaches the configured KV-cache limit.
A benchmark that uses one short prompt and one sequence can prove that a checkpoint loads, but it cannot validate a production SLO.
Convert measured throughput into inference cost
First select a topology that passes the memory check. Then obtain the current hourly price for the complete instance or fleet, including host and attached infrastructure. Use measured output throughput in the same workload that produced your latency results.
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cost_per_1M_output_tokens = hourly_cost × 1,000,000 / (measured_output_tokens_per_second × 3,600 × utilization)
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utilization is a decimal such as 0.50 or 0.70, representing the sustainable fraction of the benchmark throughput you expect to use. Include it because an always-full synthetic benchmark usually overstates real-world utilization.
Keep input and output accounting separate
If your provider bills input and output tokens at different rates, calculate both streams separately. Report input-token cost and output-token cost rather than hiding them in one blended number. A service with long prompts may be input-cost dominated even when decode throughput is high.
Include the non-GPU bill
- CPU host, system RAM, and local NVMe or network storage.
- Networking and data-transfer charges.
- Orchestration, logging, metrics, monitoring, and support.
- Idle capacity caused by minimum replica counts or slow autoscaling.
- Additional replicas for failover, rolling upgrades, or regional availability.
Calculate at least two cases: average utilization and peak-capacity operation. Autoscaling can reduce idle spend, but cold starts and replica warm-up may affect TTFT and availability.
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Optimization levers and their trade-offs
Weight quantization
FP8, INT8, and INT4 reduce resident weight memory according to the bytes-per-parameter table. Validate accuracy on your task, kernel support on the target accelerator, and compatibility with expert routing. A smaller checkpoint that lacks an efficient kernel can be slower than a larger one.
KV-cache quantization
Google documents FP8 KV-cache options, and AWS notes that converting FP16 KV storage to FP8 approximately halves KV bytes. The saving is valuable for long context and high concurrency, but measure quality and tail latency because quantized cache values can affect generation.
Context limits
Set the maximum model length to the product requirement instead of copying the model’s theoretical maximum. A large unused limit can reserve memory that would otherwise serve more simultaneous sequences. Enforce request limits so one outlier cannot exhaust the cache pool.
Parallelism and placement
Tensor and expert parallelism spread weights and cache across GPUs. Measure cross-GPU traffic, synchronization, and interconnect saturation. Pipeline parallelism can reduce per-device memory but may add bubbles and latency.
Paged or tiered KV storage
Paged caches reduce fragmentation and can make variable-length workloads easier to pack. Tiering to another memory tier can extend capacity, but transfers may increase latency; use it only when the resulting SLO remains acceptable.
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Batching and prefix caching
Continuous batching improves utilization when requests arrive steadily. Prefix caching can avoid repeating prefill work when many requests share a system prompt or document prefix. Both depend on traffic shape and can change fairness between short and long requests.
How to compare two deployment options
Do not rank options by HBM or hourly price alone. Use the same checkpoint and workload, then compare:
| Axis | What to measure |
|---|---|
| Usable memory | HBM remaining after weights, runtime allocations, activations, and the configured KV-cache pool. |
| Throughput | Measured prefill and decode tokens per second at representative concurrency. |
| Latency | TTFT, end-to-end latency, and p95/p99 tail values under load. |
| Unit economics | Input and output cost per million tokens at realistic utilization, including host and attached infrastructure. |
| Sharding complexity | Tensor/expert/pipeline parallel configuration, interconnect requirements, and operational failure modes. |
| Quality | Task accuracy and generation behavior after weight or KV quantization. |
| Operations | Regional capacity, replacement time, autoscaling behavior, and reliability. |
Common estimation errors and fixes
- Using 80B active as the weight count: retrieve total parameters from the checkpoint card and recalculate.
- Counting only weights: add KV cache, activations, runtime workspace, and safety margin.
- Sizing for one request: multiply KV requirements by concurrent sequences and include peak context.
- Treating advertised HBM as usable HBM: reserve space for the engine and allocator.
- Assuming quantization automatically preserves quality: run task-level quality tests and latency benchmarks.
- Using theoretical FLOPs for cost: plug measured output tokens per second into the cost equation.
- Quoting a GPU count without a topology: specify tensor, expert, or pipeline parallelism and the interconnect.
- Ignoring hybrid architecture details: verify which layers create KV entries and how recurrent state is stored.
- Ignoring regional pricing and capacity: obtain a current regional rate and confirm that the required multi-GPU shape is actually available.
A practical worksheet
Record the following values from the model card and your workload:
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- Hourly fleet cost and sustainable utilization
- Measured output tokens per second and, where relevant, input-token throughput
Then apply:
weights = total_parameters × weight_bits / 8
KV_cache = 2 × kv_bytes × num_layers × num_kv_heads × head_dim × context_length × concurrent_sequences
required_memory = weights + KV_cache + activations + runtime/workspace + safety_margin
output_cost_per_1M = hourly_cost × 1,000,000 / (output_tokens_per_second × 3,600 × utilization)
Replace every model-specific input with measured or documented values. If any of those inputs is unknown, report a range and label the missing value rather than presenting a single precise GPU count or dollar figure.
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