Optimal hybrid dual-pool ratio (--mamba-full-memory-ratio)

A hybrid model (attention layers + linear-attention layers — the recurrent-state family: Mamba/SSM, GDN, KDA, etc.) splits serving memory into two independently-budgeted pools, fixed once at startup:

• state pool (the linear-attention recurrent state) → caps concurrency (hard: whole slots, worst-case reserved, fail-loud)
• full-KV pool (attention KV) → caps context × concurrency (soft: paged, over-committable via retraction)

--mamba-full-memory-ratio r splits the post-weight budget: mamba_budget = rest · r/(1+r), i.e. mamba_budget : kv_budget = r. This skill picks the r (or the pin---max-mamba-cache-size alternative) at which neither pool bottlenecks first for the user's workload.

The formula

r*  =  (S + D) · token_equiv · dcp_size / L
token_equiv  =  state_bytes_per_slot / kv_bytes_per_token

• L = average context length per request (input + output tokens)
• token_equiv = full-KV token-equivalent of one state slot
• S = state slots per running request (cache-strategy dependent, table below)
• D = --speculative-num-draft-tokens (0 if NOSPEC); each running req carries D extra intermediate states
• dcp_size = --dcp-size (1 without DCP). DCP shards the per-rank KV by dcp_size, so KV gets ~dcp_size× cheaper per request → the balance shifts that much toward the state pool.

r is dimensionless (just the split). To also predict the actual concurrency you need rest (below).

Inputs to collect from the user

1. L — average context (input + output) in tokens.
2. The two per-GPU byte constants — one of:
   • (a) measured (preferred, exact) — from one boot log at any ratio:
     • Mamba Cache is allocated. ... ssm_state size X GB with max_mamba_cache_size: N → state_bytes_per_slot = X / N
     • KV Cache is allocated. #tokens: M, KV size: Y GB → kv_bytes_per_token = Y / M
   • (b) derived — model arch (linear-layer count + state dims d_state/d_conv/heads/head_dim; attention type + KV dims: MLA latent dim, or GQA kv_heads·head_dim·layers) × the dtypes below.
3. S — from --mamba-radix-cache-strategy, the overlap scheduler, and SGLANG_OPT_MAMBA_SKIP_DECODE_LOCK (table below).
4. D — --speculative-num-draft-tokens (0 if NOSPEC; also 0 when ReplaySSM spec-verify is enabled — see caveats).
5. dcp_size — --dcp-size (1 if no DCP). If DCP and spec with a replicated draft KV, apply the draft caveat (below).
6. KV dtype (bf16 / fp8) and ssm dtype (fp32 / bf16) — they set the two byte constants (see the token_equiv 2×2).
7. (only to also predict the clamp, not just r) rest = per-GPU memory − weights at the chosen --mem-fraction-static. Read avail mem after Load weight end, or Memory pool end. avail mem + pool sizes, from the boot log.

S (state slots per running request), set by --mamba-radix-cache-strategy

All strategies keep prefix caching on; they differ in the track buffer that snapshots chunk-boundary state under the overlap scheduler. S = base + ping-pong, where base is 3 (live state + radix retention/COW headroom), and ping-pong = 2 (overlap on, non-lazy) / 1 (lazy, or overlap off) / 0 (no track buffer at all). This mirrors kv_cache_configurator._calculate_mamba_ratio; read it there if a release moves the constants.

SGLANG_OPT_MAMBA_SKIP_DECODE_LOCK=1 takes the base to 2, since the decode-time skip frees one resident slot per running request. no_buffer is the exception and stays at an effective 3: it adds that slot straight back, because its binding limit is the prefill-to-decode peak, which a decode-time change does not shrink.

The skip is a real concurrency lever, not a rounding detail: at the default strategy it takes S from 5 to 4, so the same state budget admits 25% more requests and r* drops by the same factor.

Overlap counts as off when --disable-overlap-schedule is passed or --pp-size > 1 (pipeline parallelism turns it off for you). So a PP deployment left on the default strategy sits on the S = 4 row, not S = 5 — using 5 there overstates the state cost and starves KV.

token_equiv — compute it from the two byte constants

token_equiv = state_bytes_per_slot / kv_bytes_per_token. Both bytes are per-model, per-GPU — read them from a boot log (or derive from the arch + dtypes) and divide. That's the whole thing; the dtype effects below are just for a quick mental estimate.

The recurrent state has two parts: state_bytes = SSM_bytes + conv_bytes. The SSM part scales with --mamba-ssm-dtype; the conv part is always bf16 (SGLANG_MAMBA_CONV_DTYPE, fixed). How the dtype knobs move token_equiv:

• fp8 KV → ×2 (universal, exact): fp8 is 1 byte vs bf16's 2, so kv_bytes_per_token halves.
• fp32 ssm vs bf16 ssm → ×≈2: only the SSM tensor doubles, conv stays fixed, so the exact factor is (2·SSM + conv)/(SSM + conv) — just under 2. conv is usually small relative to SSM, so estimate ≈2 (e.g. one measured model: conv ≈7% of state → factor ≈1.9). Only bother with the exact split if a model's conv is unusually large.
• fp32 ssm + fp8 KV → ×≈4.

For an actual number, don't apply factors — read state_bytes_per_slot and kv_bytes_per_token for your model+dtype from the boot log and divide.

The GB the boot log prints for both pools is GiB (1024³). Convert both the same way and token_equiv is unaffected either way, since it is a ratio — but get it right before comparing an absolute byte figure against your own log.

Worked example (one measured model, TP8, fp32 ssm + fp8 KV), read off one boot log:

Mamba Cache is allocated. max_mamba_cache_size: 257, conv_state size: 0.46GB, ssm_state size: 13.04GB
KV Cache is allocated. dtype: torch.float8_e4m3fn, #tokens: 1167552, KV size: 15.03 GB

state_bytes_per_slot = (0.46 + 13.04) GiB / 257 ≈ 56.4 MB, kv_bytes_per_token = 15.03 GiB / 1167552 ≈ 13.8 KB → token_equiv ≈ **4080**.

Compute

def optimal_ratio(L, state_bytes_per_slot, kv_bytes_per_token, S, D=0, dcp_size=1):
    token_equiv = state_bytes_per_slot / kv_bytes_per_token
    r = (S + D) * token_equiv * dcp_size / L
    return r  # value of --mamba-full-memory-ratio (>1 is legal)

def predict_clamp(rest_bytes, r, state_bytes_per_slot, S, D=0):
    mamba_budget = rest_bytes * r / (1 + r)
    slots = mamba_budget / state_bytes_per_slot
    # spec: each running req reserves (S+D) worth; non-spec just S
    return int(slots // S)  # NOSPEC; with spec the budget joint-solves for (S+D)·per_req per req

Then state the result three ways: the r value, the predicted clamp (if rest given), and which pool binds (min(mamba_clamp, KV_cap), where KV_cap = kv_tokens · dcp_size / L).

Procedure

1. Free lever first: if the boot log shows large idle in Memory pool end. avail mem (e.g. 30–40 GB at mem-frac 0.85), raise --mem-fraction-static (→0.92) before touching the split — it grows rest for both pools at no cost. Validate graph-capture headroom once.
2. Collect the inputs. Prefer a real boot log for the two byte constants.
3. Compute r*. If r* would drive the state pool below one request's worth (mamba_budget < S · per_req, happens at very long L), switch to pinning --max-mamba-cache-size = target_concurrency · S and let the rest go to KV — a sub-0.15 r is fragile.
4. Report r, predicted clamp, binding pool, and any dtype accuracy gate that applies.

Worked examples (TP8, B300, validated against measured clamps)

Constants (from boot logs): state_bytes_per_slot ≈ 56.4 MB (fp32 ssm), kv_bytes_per_token ≈ 13.8 KB (fp8) → token_equiv ≈ 4080. Cache strategy extra_buffer_lazy → S = 4. Workload L = 9216 (8192 in + 1024 out). NOSPEC → D = 0.

• TP (dcp_size=1): r = 4 · 4080 · 1 / 9216 ≈ 1.8. Measured: clamp 88, KV cap 87 → balanced. ✅
• DCP8 (dcp_size=8): r = 4 · 4080 · 8 / 9216 ≈ 14. Measured at r=14: clamp 125, KV cap 129 → balanced. ✅ (At the naive r=1.8 the DCP KV pool is ~8× over-provisioned — cap 683 vs clamp 86 — wasting budget that should go to the state pool.)

predict_clamp on the same model at the default extra_buffer, overlap on and the decode-lock skip off (S = 5), three boot logs:

Reference r for this example model (token_equiv ≈ 4080, i.e. fp32 ssm + fp8 KV; S=5 default; multiply by (S+D)/S for spec, by dcp_size for DCP) — recompute with your own token_equiv for a different model:

Caveats

• DCP + spec: a replicated (non-DCP-sharded) draft KV does not shard by dcp_size; at long L it dominates per-token cost, so the clean ×dcp_size overstates DCP's advantage — fall back to a per-token direct-solve (KV term /dcp + an un-sharded draft-KV term) when spec is on. (NOSPEC → clean ×dcp_size holds.)
• Spec + ReplaySSM → use D=0, not the draft-token count. When ReplaySSM spec-verify is enabled, the D intermediate SSM states move off the per-request slot budget onto a fixed ring (a one-time deduction from rest, not a per-req term). So the mamba-slot cost per running request drops back to S (clamp = mmcs / S, not /(S+D)), and the balance ratio returns to the NOSPEC value (r* ≈ S·token_equiv·dcp/L). Measured example (TP8, D=8, L≈9K): applying D=8 computes r*≈2.6 but the true optimum is r≈1.0 — D=8 lands KV-bound at ~45% below the achievable peak concurrency. Plain spec (no replayssm) keeps D = the draft-token count.
• Under DCP the balance point sits beyond any realistic L (the state pool binds first for almost everything), so the practical recommendation is to pin --max-mamba-cache-size = target_concurrency · S directly rather than dial a large r.
• --max-mamba-cache-size overrides r; bytes beyond the r budget come out of the KV pool one-for-one.
• Precision changes are behind accuracy gates: --kv-cache-dtype fp8_e4m3 (doubles token_equiv → doubles r) and --mamba-ssm-dtype bfloat16 (~halves token_equiv; also silently switches the linear-attention decode backend on SM100+ — pin --linear-attn-decode-backend triton) shift outputs; validate accuracy for the workload before production.
• Asymmetry: the state pool is worst-case-reserved and fail-loud; KV degrades gracefully (retraction). When L is spiky/uncertain, bias r up rather than starve the state pool.